Activity report
RNSR: 201421111R
Research center
Team name:
Static Analysis by Abstract Interpretation
In collaboration with:
Département d'Informatique de l'Ecole Normale Supérieure
Algorithmics, Programming, Software and Architecture
Proofs and Verification
Creation of the Team: 2014 January 01, updated into Project-Team: 2015 April 01


Computer Science and Digital Science

  • A2. Software
  • A2.1. Programming Languages
  • A2.1.1. Semantics of programming languages
  • A2.1.7. Distributed programming
  • A2.1.12. Dynamic languages
  • A2.2.1. Static analysis
  • A2.3. Embedded and cyber-physical systems
  • A2.3.1. Embedded systems
  • A2.3.2. Cyber-physical systems
  • A2.3.3. Real-time systems
  • A2.4. Formal method for verification, reliability, certification
  • A2.4.1. Analysis
  • A2.4.2. Model-checking
  • A2.4.3. Proofs
  • A2.6.1. Operating systems
  • A4.4. Security of equipment and software
  • A4.5. Formal methods for security

Other Research Topics and Application Domains

  • B1.1. Biology
  • B1.1.8. Mathematical biology
  • B1.1.10. Systems and synthetic biology
  • B5.2. Design and manufacturing
  • B5.2.1. Road vehicles
  • B5.2.2. Railway
  • B5.2.3. Aviation
  • B5.2.4. Aerospace
  • B6.1. Software industry
  • B6.1.1. Software engineering
  • B6.1.2. Software evolution, maintenance
  • B6.6. Embedded systems

1 Team members, visitors, external collaborators

Research Scientists

  • Xavier Rival [Team leader, Inria, Senior Researcher, HDR]
  • Vincent Danos [CNRS, Senior Researcher, HDR]
  • Cezara Dragoi [Inria, Researcher, until Aug 2020]
  • Jérôme Feret [Inria, Researcher]
  • Caterina Urban [Inria, Researcher]

Post-Doctoral Fellows

  • Hamza El Khalloufi [Inria, from Oct 2020]
  • Adrien Husson [Inria]

PhD Students

  • Marc Chevalier [École Normale Supérieure de Paris, until Nov 2020]
  • Josselin Giet [École Normale Supérieure de Paris, from Sep 2020]
  • Patricio Inzaghi [Inria]
  • Denis Mazzucato [Inria, from Oct 2020]
  • Olivier Nicole [CEA]
  • Albin Salazar [Inria]
  • Ignacio Tiraboschi [Inria, from Sep 2020]

Technical Staff

  • Tie Cheng [Inria, Engineer]
  • Yves Stan Le Cornec [Inria, Engineer, until Mar 2020]
  • Sebastien Légaré [Inria, Engineer, from Mar 2020]
  • Thierry Martinez [Inria, Engineer]
  • Anfu Tang [Inria, Engineer, Feb 2020]

Interns and Apprentices

  • Alain Delaet–Tixeuil [École Normale Supérieure de Lyon, from Mar 2020 until Jul 2020]
  • Serge Durand [École Normale Supérieure de Paris, from Jun 2020 until Aug 2020]
  • Noémie Fong [École Normale Supérieure de Paris, Intern, From October 2020 until July 2021, Part time]
  • Noémie Fong [École Normale Supérieure de Paris, Intern, From March 2020 until July 2020]
  • Octave Hazard [École Normale Supérieure de Paris, Intern, From April 2020 until August 2020]

Administrative Assistant

  • Nathalie Gaudechoux [Inria]

Visiting Scientist

  • Marco Zanella [Université de Padoue - Italie, from May 2020 until Aug 2020]

External Collaborator

  • Pierre Boutillier [Self-employed]

2 Overall objectives

Our group focuses on developing automated techniques to compute semantic properties of programs and other systems with a computational semantics in general. Such properties include (but are not limited to) important classes of correctness properties.

Verifying safety critical systems (such as avionics systems) is an important motivation to compute such properties. Indeed, a fault in an avionics system, such as a runtime error in the fly-by-wire command software, may cause an accident, with loss of life. As these systems are also very complex and are developed by large teams and maintained over long periods, their verification has become a crucial challenge. Safety critical systems are not limited to avionics: software runtime errors in cruise control management systems were recently blamed for causing unintended acceleration in certain Toyota models (the case was settled with a 1.2 billion dollars fine in March 2014, after years of investigation and several trials). Similarly, other transportation systems (railway), energy production systems (nuclear power plants, power grid management), medical systems (pacemakers, surgery and patient monitoring systems), and value transfers in decentralized systems (smart contracts), rely on complex software, which should be verified.

Beyond the field of embedded systems, other pieces of software may cause very significant harm in the case of bugs, as demonstrated by the Heartbleed security hole: due to a wrong protocol implementation, many websites could leak private information, over years.

An important example of semantic properties is the class of safety properties. A safety property typically specifies that some (undesirable) event will never occur, whatever the execution of the program that is considered. For instance, the absence of runtime error is a very important safety property. Other important classes of semantic properties include liveness properties (i.e., properties that specify that some desirable event will eventually occur) such as termination and security properties, such as the absence of information flows from private to public channels.

All these software semantic properties are not decidable, as can be shown by reduction to the halting problem. Therefore, there is no chance to develop any fully automatic technique able to decide, for any system, whether or not it satisfies some given semantic property.

The classic development techniques used in industry involve testing, which is not sound, as it only gives information about a usually limited test sample: even after successful test-based validation, situations that were untested may generate a problem. Furthermore, testing is costly in the long term, as it should be re-done whenever the system to verify is modified. Machine-assisted verification is another approach which verifies human specified properties. However, this approach also presents a very significant cost, as the annotations required to verify large industrial applications would be huge.

By contrast, the antique group focuses on the design of semantic analysis techniques that should be sound (i.e., compute semantic properties that are satisfied by all executions) and automatic (i.e., with no human interaction), although generally incomplete (i.e., not able to compute the best —in the sense of: most precise— semantic property). As a consequence of incompleteness, we may fail to verify a system that is actually correct. For instance, in the case of verification of absence of runtime error, the analysis may fail to validate a program, which is safe, and emit false alarms (that is reports that possibly dangerous operations were not proved safe), which need to be discharged manually. Even in this case, the analysis provides information about the alarm context, which may help disprove it manually or refine the analysis.

The methods developed by the antique group are not limited to the analysis of software. We also consider complex biological systems (such as models of signaling pathways, i.e. cascades of protein interactions, which enable signal communication among and within cells), described in higher level languages, and use abstraction techniques to reduce their combinatorial complexity and capture key properties so as to get a better insight in the underlying mechanisms of these systems.

3 Research program

3.1 Semantics

Semantics plays a central role in verification since it always serves as a basis to express the properties of interest, that need to be verified, but also additional properties, required to prove the properties of interest, or which may make the design of static analysis easier.

For instance, if we aim for a static analysis that should prove the absence of runtime error in some class of programs, the concrete semantics should define properly what error states and non error states are, and how program executions step from a state to the next one. In the case of a language like C, this includes the behavior of floating point operations as defined in the IEEE 754 standard. When considering parallel programs, this includes a model of the scheduler, and a formalization of the memory model.

In addition to the properties that are required to express the proof of the property of interest, it may also be desirable that semantics describe program behaviors in a finer manner, so as to make static analyses easier to design. For instance, it is well known that, when a state property (such as the absence of runtime error) is valid, it can be established using only a state invariant (i.e., an invariant that ignores the order in which states are visited during program executions). Yet searching for trace invariants (i.e., that take into account some properties of program execution history) may make the static analysis significantly easier, as it will allow it to make finer case splits, directed by the history of program executions. To allow for such powerful static analyses, we often resort to a non standard semantics, which incorporates properties that would normally be left out of the concrete semantics.

3.2 Abstract interpretation and static analysis

Once a reference semantics has been fixed and a property of interest has been formalized, the definition of a static analysis requires the choice of an abstraction. The abstraction ties a set of abstract predicates to the concrete ones, which they denote. This relation is often expressed with a concretization function that maps each abstract element to the concrete property it stands for. Obviously, a well chosen abstraction should allow one to express the property of interest, as well as all the intermediate properties that are required in order to prove it (otherwise, the analysis would have no chance to achieve a successful verification). It should also lend itself to an efficient implementation, with efficient data-structures and algorithms for the representation and the manipulation of abstract predicates. A great number of abstractions have been proposed for all kinds of concrete data types, yet the search for new abstractions is a very important topic in static analysis, so as to target novel kinds of properties, to design more efficient or more precise static analyses.

Once an abstraction is chosen, a set of sound abstract transformers can be derived from the concrete semantics and that account for individual program steps, in the abstract level and without forgetting any concrete behavior. A static analysis follows as a result of this step by step approximation of the concrete semantics, when the abstract transformers are all computable. This process defines an abstract interpretation  30. The case of loops requires a bit more work as the concrete semantics typically relies on a fixpoint that may not be computable in finitely many iterations. To achieve a terminating analysis we then use widening operators  30, which over-approximate the concrete union and ensure termination.

A static analysis defined that way always terminates and produces sound over-approximations of the programs behaviors. Yet, these results may not be precise enough for verification. This is where the art of static analysis design comes into play through, among others:

  • the use of more precise, yet still efficient enough abstract domains;
  • the combination of application-specific abstract domains;
  • the careful choice of abstract transformers and widening operators.

3.3 Applications of the notion of abstraction in semantics

In the previous subsections, we sketched the steps in the design of a static analyzer to infer some family of properties, which should be implementable, and efficient enough to succeed in verifying non trivial systems.

The same principles can be applied successfully to other goals. In particular, the abstract interpretation framework should be viewed as a very general tool to compare different semantics, not necessarily with the goal of deriving a static analyzer. Such comparisons may be used in order to prove two semantics equivalent (i.e., one is an abstraction of the other and vice versa), or that a first semantics is strictly more expressive than another one (i.e., the latter can be viewed an abstraction of the former, where the abstraction actually makes some information redundant, which cannot be recovered). A classical example of such comparison is the classification of semantics of transition systems  29, which provides a better understanding of program semantics in general. For instance, this approach can be applied to get a better understanding of the semantics of a programming language, but also to select which concrete semantics should be used as a foundation for a static analysis, or to prove the correctness of a program transformation, compilation or optimization.

3.4 From properties to explanations

In many application domains, we can go beyond the proof that a program satisfies its specification. Abstractions can also offer new perspectives to understand how complex behaviors of programs emerge from simpler computation steps. Abstractions can be used to find compact and readable representations of sets of traces, causal relations, and even proofs. For instance, abstractions may decipher how the collective behaviors of agents emerge from the orchestration of their individual ones in distributed systems (such as consensus protocols, models of signaling pathways). Another application is the assistance for the diagnostic of alarms of a static analyzer.

Complex systems and software have often times intricate behaviors, leading to executions that are hard to understand for programmers and also difficult to reason about with static analyzers. Shared memory and distributed systems are notorious for being hard to reason about due to the interleaving of actions performed by different processes and the non-determinism of the network that might lose, corrupt, or duplicate messages. Reduction theorems, e.g., Lipton's theorem, have been proposed to facilitate reasoning about concurrency, typically transforming a system into one with a coarse-grained semantics that usually increases the atomic sections. We investigate reduction theorems for distributed systems and ways to compute the coarse-grained counter part of a system automatically. Compared with shared memory concurrency, automated methods to reason about distributed systems have been less investigated in the literature. We take a programming language approach based on high-level programming abstractions. We focus on partially-synchronous communication closed round-based models, introduced in the distributed algorithms community for its simpler proof arguments. The high-level language is compiled into a low-level (asynchronous) programming language. Conversely, systems defined under asynchronous programming paradigms are decompiled into the high-level programming abstractions. The correctness of the compilation/decompilation process is based on reduction theorems (in the spirit of Lipton and Elrad-Francez) that preserve safety and liveness properties.

In models of signaling pathways, collective behavior emerges from competition for common resources, separation of scales (time/concentration), non linear feedback loops, which are all consequences of mechanistic interactions between individual bio-molecules (e.g., proteins). While more and more details about mechanistic interactions are available in the literature, understanding the behavior of these models at the system level is far from easy. Causal analysis helps explaining how specific events of interest may occur. Model reduction techniques combine methods from different domains such as the analysis of information flow used in communication protocols, and tropicalization methods that comes from physics. The result is lower dimension systems that preserve the behavior of the initial system while focusing of the elements from which emerges the collective behavior of the system.

The abstraction of causal traces offer nice representation of scenarios that lead to expected or unexpected events. This is useful to understand the necessary steps in potential scenarios in signaling pathways; this is useful as well to understand the different steps of an intrusion in a protocol. Lastly, traces of computation of a static analyzer can themselves be abstracted, which provides assistance to classify true and false alarms. Abstracted traces are symbolic and compact representations of sets of counter-examples to the specification of a system which help one to either understand the origin of bugs, or to find that some information has been lost in the abstraction leading to false alarms.

4 Application domains

4.1 Verification of safety critical embedded software

The verification of safety critical embedded software is a very important application domain for our group. First, this field requires a high confidence in software, as a bug may cause disastrous events. Thus, it offers an obvious opportunity for a strong impact. Second, such software usually have better specifications and a better design than many other families of software, hence are an easier target for developing new static analysis techniques (which can later be extended for more general, harder to cope with families of programs). This includes avionics, automotive and other transportation systems, medical systems ...

For instance, the verification of avionics systems represent a very high percentage of the cost of an airplane (about 30 % of the overall airplane design cost). The state of the art development processes mainly resort to testing in order to improve the quality of software. Depending on the level of criticality of a software (at the highest levels, any software failure would endanger the flight) a set of software requirements are checked with test suites. This approach is both costly (due to the sheer amount of testing that needs to be performed) and unsound (as errors may go unnoticed, if they do not arise on the test suite).

By contrast, static analysis can ensure higher software quality at a lower cost. Indeed, a static analyzer will catch all bugs of a certain kind. Moreover, a static analysis run typically lasts a few hours, and can be integrated in the development cycle in a seamless manner. For instance, Astrée successfully verified the absence of runtime error in several families of safety critical fly-by-wire avionic software, in at most a day of computation, on standard hardware. Other kinds of synchronous embedded software have also been analyzed with good results.

In the future, we plan to greatly extend this work so as to verify other families of embedded software (such as communication, navigation and monitoring software) and other families of properties (such as security and liveness properties).

Embedded software in charge of communication, navigation, and monitoring typically relies on a parallel structure, where several threads are executed concurrently, and manage different features (input, output, user interface, internal computation, logging ...). This structure is also often found in automotive software. An even more complex case is that of distributed systems, where several separate computers are run in parallel and take care of several sub-tasks of a same feature, such as braking. Such a logical structure is not only more complex than the synchronous one, but it also introduces new risks and new families of errors (deadlocks, data-races...). Moreover, such less well designed, and more complex embedded software often utilizes more complex data-structures than synchronous programs (which typically only use arrays to store previous states) and may use dynamic memory allocation, or build dynamic structures inside static memory regions, which are actually even harder to verify than conventional dynamically allocated data structures. Complex data-structures also introduce new kinds of risks (the failure to maintain structural invariants may lead to runtime errors, non termination, or other software failures). To verify such programs, we will design additional abstract domains, and develop new static analysis techniques, in order to support the analysis of more complex programming language features such as parallel and concurrent programming with threads and manipulations of complex data structures. Due to their size and complexity, the verification of such families of embedded software is a major challenge for the research community.

Furthermore, embedded systems also give rise to novel security concerns. It is in particular the case for some aircraft-embedded computer systems, which communicate with the ground through untrusted communication media. Besides, the increasing demand for new capabilities, such as enhanced on-board connectivity, e.g. using mobile devices, together with the need for cost reduction, leads to more integrated and interconnected systems. For instance, modern aircrafts embed a large number of computer systems, from safety-critical cockpit avionics to passenger entertainment. Some systems meet both safety and security requirements. Despite thorough segregation of subsystems and networks, some shared communication resources raise the concern of possible intrusions. Because of the size of such systems, and considering that they are evolving entities, the only economically viable alternative is to perform automatic analyses. Such analyses of security and confidentiality properties have never been achieved on large-scale systems where security properties interact with other software properties, and even the mapping between high-level models of the systems and the large software base implementing them has never been done and represents a great challenge. Our goal is to prove empirically that the security of such large scale systems can be proved formally, thanks to the design of dedicated abstract interpreters.

The long term goal is to make static analysis more widely applicable to the verification of industrial software.

4.2 Static analysis of software components and libraries

An important goal of our work is to make static analysis techniques easier to apply to wider families of software. Then, in the longer term, we hope to be able to verify less critical, yet very commonly used pieces of software. Those are typically harder to analyze than critical software, as their development process tends to be less rigorous. In particular, we will target operating systems components and libraries. As of today, the verification of such programs is considered a major challenge to the static analysis community.

As an example, most programming languages offer Application Programming Interfaces (API) providing ready-to-use abstract data structures (e.g., sets, maps, stacks, queues, etc.). These APIs, are known under the name of containers or collections, and provide off-the-shelf libraries of high level operations, such as insertion, deletion and membership checks. These container libraries give software developers a way of abstracting from low-level implementation details related to memory management, such as dynamic allocation, deletion and pointer handling or concurrency aspects, such as thread synchronization. Libraries implementing data structures are important building bricks of a huge number of applications, therefore their verification is paramount. We are interested in developing static analysis techniques that will prove automatically the correctness of large audience libraries such as Glib and Threading Building Blocks.

4.3 Models of mechanistic interactions between proteins

Computer Science takes a more and more important role in the design and the understanding of biological systems such as signaling pathways, self assembly systems, DNA repair mechanisms. Biology has gathered large data-bases of facts about mechanistic interactions between proteins, but struggles to draw an overall picture of how these systems work as a whole. High level languages designed in Computer Science allow one to collect these interactions in integrative models, and provide formal definitions (i.e., semantics) for the behavior of these models. This way, modelers can encode their knowledge, following a bottom-up discipline, without simplifying a priori the models at the risk of damaging the key properties of the system. Yet, the systems that are obtained this way suffer from combinatorial explosion (in particular, in the number of different kinds of molecular components, which can arise at run-time), which prevents from a naive computation of their behavior.

We develop various analyses based on abstract interpretation, and tailored to different phases of the modeling process. We propose automatic static analyses in order to detect inconsistencies in the early phases of the modeling process. These analyses are similar to the analysis of classical safety properties of programs. They involve both forward and backward reachability analyses as well as causality analyses, and can be tuned at different levels of abstraction. We also develop automatic static analyses in order to identify key elements in the dynamics of these models. The results of these analyses are sent to another tool, which is used to automatically simplify models. The correctness of this simplification process is proved by the means of abstract interpretation: this ensures formally that the simplification preserves the quantitative properties that have been specified beforehand by the modeler. The whole pipeline is parameterized by a large choice of abstract domains which exploits different features of the high level description of models.

4.4 Consensus

Fault-tolerant distributed systems provide a dependable service on top of unreliable computers and networks. Famous examples are geo-replicated data-bases, distributed file systems, or blockchains. Fault-tolerant protocols replicate the system and ensure that all (unreliable) replicas are perceived from the outside as one single reliable machine. To give the illusion of a single reliable machine “consensus” protocols force replicas to agree on the “current state” before making this state visible to an outside observer. We are interested in (semi-)automatically proving the total correctness of consensus algorithms in the benign case (messages are lost or processes crash) or the Byzantine case (processes may lie about their current state). In order to do this, we first define new reduction theorems to simplify the behaviors of the system and, second, we introduce new static analysis methods to prove the total correctness of adequately simplified systems. We focus on static analysis based Satisfiability Modulo Theories (SMT) solvers which offers a good compromise between automation and expressiveness. Among our benchmarks are Paxos, PBFT (Practical Byzantine Fault-Tolerance), and blockchain algorithms (Red-Belly, Tendermint, Algorand). These are highly challenging benchmarks, with a lot of non-determinism coming from the interleaving semantics and from the adversarial environment in which correct processes execute, environment that can drop messages, corrupt them, etc. Moreover, these systems were originally designed for a few servers but today are deployed on networks with thousands of nodes. The “optimizations” for scalability can no longer be overlooked and must be considered as integral part of the algorithms, potentially leading to specifications weaker than the so much desired consensus.

4.5 Smart contracts

Blockchain applications in finance have emerged in 2020 as the lead applications. The new field called decentralised finance (or also open finance) re-creates basic financial functionalities such as ireeversible and reverible swaps of assets. There are broad goals to our research in this emerging area: structuring contract languages which guarantee good execution properties by construction, and finding mechanisms that foster liquidity.

We are investigating several other problems in decentralised finance: protocols for capital-efficient decentralised exchanges; general convex problems for the optimal routing and arbitrage in the network of exchange platforms; and the economics of the competition between two-sided exchange platforms.

4.6 Staticanalysis of data science software

Nowadays, thanks to advances in machine learning and the availability of vast amounts of data, computer software plays an increasingly important role in assisting or even autonomously performing tasks in our daily lives. As data science software becomes more and more widespread, we become increasingly vulnerable to programming errors. In particular, programming errors that do not cause failures can have serious consequences since code that produces an erroneous but plausible result gives no indication that something went wrong. This issue becomes particularly worrying knowing that machine learning software, thanks to its ability to efficiently approximate or simulate more complex systems, is slowly creeping into mission critical scenarios. However, programming errors are not the only concern. Another important issue is the vulnerability of machine learning models to adversarial examples, that is, small input perturbations that cause the model to misbehave in unpredictable ways. More generally, a critical issue is the notorious difficulty to interpret and explain machine learning software. Finally, as we are witnessing widespread adoption of software with far-reaching societal impact — i.e., to automate decision-making in fields such as social welfare, criminal justice, and even health care — a number of recent cases have evidenced the importance of ensuring software fairness as well as data privacy. Going forward, data science software will be subject to more and more legal regulations (e.g., the European General Data Protection Regulation adopted in 2016) as well as administrative audits.

It is thus paramount to develop method and tools that can keep up with these developments and enhance our understanding of data science software and ensure it behaves correctly and reliably. In particular, we are interesting in developing new static analyses specifically tailored to the idiosyncrasies of data science software. This makes it a new and exciting area for static analysis, offering a wide variety of challenging problems with huge potential impact on various interdisciplinary application domains 32.

5 Social and environmental responsibility

5.1 Impact of research results

We are advising several companies such as Bender operating on the Tezos blockchain, think tanks such as the CDC Labchain (Caisse des Dépots), and other informal development groups such as Jaxnet on decentralised finance protocols and mechanism design for consensus incentives.

We are advising static analysis companies including AbsInt Angewandte Informatik (static analysis for the verification of embedded software) and MatrixLead (static analysis for spreadsheet applications).

6 Highlights of the year

In 2020, and after several years of intense preparation, the book “Introduction to static analysis: an abstract interpretation perspective” 21 was published at MIT Press, so as to disseminate more broadly foundations of abstract interpretation and of static analysis, to not only researchers, professors, and students but also working developers, engineers, and software verification experts.

7 New software and platforms

7.1 New software

7.1.1 APRON

  • Scientific Description: The APRON library is intended to be a common interface to various underlying libraries/abstract domains and to provide additional services that can be implemented independently from the underlying library/abstract domain, as shown by the poster on the right (presented at the SAS 2007 conference. You may also look at:
  • Functional Description: The Apron library is dedicated to the static analysis of the numerical variables of a program by abstract interpretation. Its goal is threefold: provide ready-to-use numerical abstractions under a common API for analysis implementers, encourage the research in numerical abstract domains by providing a platform for integration and comparison of domains, and provide a teaching and demonstration tool to disseminate knowledge on abstract interpretation.
  • URL: http://apron.cri.ensmp.fr/library/
  • Author: Bertrand Jeannet
  • Contacts: Antoine Miné, Bertrand Jeannet
  • Participants: Antoine Miné, Bertrand Jeannet

7.1.2 Astrée

  • Name: The AstréeA Static Analyzer of Asynchronous Software
  • Keywords: Static analysis, Static program analysis, Program verification, Software Verification, Abstraction
  • Scientific Description:

    Astrée analyzes structured C programs, with complex memory usages, but without dynamic memory allocation nor recursion. This encompasses many embedded programs as found in earth transportation, nuclear energy, medical instrumentation, and aerospace applications, in particular synchronous control/command. The whole analysis process is entirely automatic.

    Astrée discovers all runtime errors including:

    undefined behaviors in the terms of the ANSI C99 norm of the C language (such as division by 0 or out of bounds array indexing),

    any violation of the implementation-specific behavior as defined in the relevant Application Binary Interface (such as the size of integers and arithmetic overflows),

    any potentially harmful or incorrect use of C violating optional user-defined programming guidelines (such as no modular arithmetic for integers, even though this might be the hardware choice),

    failure of user-defined assertions.

  • Functional Description:

    Astrée analyzes structured C programs, with complex memory usages, but without dynamic memory allocation nor recursion. This encompasses many embedded programs as found in earth transportation, nuclear energy, medical instrumentation, and aerospace applications, in particular synchronous control/command. The whole analysis process is entirely automatic.

    Astrée discovers all runtime errors including: - undefined behaviors in the terms of the ANSI C99 norm of the C language (such as division by 0 or out of bounds array indexing), - any violation of the implementation-specific behavior as defined in the relevant Application Binary Interface (such as the size of integers and arithmetic overflows), - any potentially harmful or incorrect use of C violating optional user-defined programming guidelines (such as no modular arithmetic for integers, even though this might be the hardware choice), - failure of user-defined assertions.

    Astrée is a static analyzer for sequential programs based on abstract interpretation. The Astrée static analyzer aims at proving the absence of runtime errors in programs written in the C programming language.

  • URL: http://www.astree.ens.fr/
  • Contacts: Patrick Cousot, Radhia Cousot, Jérôme Feret, Xavier Rival, Antoine Miné
  • Participants: Antoine Miné, Jérôme Feret, Laurent Mauborgne, Patrick Cousot, Radhia Cousot, Xavier Rival
  • Partners: CNRS, ENS Paris, AbsInt Angewandte Informatik GmbH

7.1.3 AstréeA

  • Name: The AstréeA Static Analyzer of Asynchronous Software
  • Keywords: Static analysis, Static program analysis
  • Scientific Description: AstréeA analyzes C programs composed of a fixed set of threads that communicate through a shared memory and synchronization primitives (mutexes, FIFOs, blackboards, etc.), but without recursion nor dynamic creation of memory, threads nor synchronization objects. AstréeA assumes a real-time scheduler, where thread scheduling strictly obeys the fixed priority of threads. Our model follows the ARINC 653 OS specification used in embedded industrial aeronautic software. Additionally, AstréeA employs a weakly-consistent memory semantics to model memory accesses not protected by a mutex, in order to take into account soundly hardware and compiler-level program transformations (such as optimizations). AstréeA checks for the same run-time errors as Astrée , with the addition of data-races.
  • Functional Description: AstréeA is a static analyzer prototype for parallel software based on abstract interpretation. The AstréeA prototype is a fork of the Astrée static analyzer that adds support for analyzing parallel embedded C software.
  • URL: http://www.astreea.ens.fr/
  • Contacts: Patrick Cousot, Radhia Cousot, Xavier Rival, Jérôme Feret, Antoine Miné
  • Participants: Antoine Miné, Jérôme Feret, Patrick Cousot, Radhia Cousot, Xavier Rival
  • Partners: CNRS, ENS Paris, AbsInt Angewandte Informatik GmbH

7.1.4 ClangML

  • Keyword: Compilation
  • Functional Description: ClangML is an OCaml binding with the Clang front-end of the LLVM compiler suite. Its goal is to provide an easy to use solution to parse a wide range of C programs, that can be called from static analysis tools implemented in OCaml, which allows to test them on existing programs written in C (or in other idioms derived from C) without having to redesign a front-end from scratch. ClangML features an interface to a large set of internal AST nodes of Clang , with an easy to use API. Currently, ClangML supports all C language AST nodes, as well as a large part of the C nodes related to C++ and Objective-C.
  • URL: https://github.com/Antique-team/clangml/tree/master/clang
  • Contacts: Xavier Rival, François Berenger, Devin Mccoughlin, Pippijn Van Steenhoven
  • Participants: Devin Mccoughlin, François Berenger, Pippijn Van Steenhoven

7.1.5 FuncTion

  • Scientific Description: FuncTion is based on an extension to liveness properties of the framework to analyze termination by abstract interpretation proposed by Patrick Cousot and Radhia Cousot. FuncTion infers ranking functions using piecewise-defined abstract domains. Several domains are available to partition the ranking function, including intervals, octagons, and polyhedra. Two domains are also available to represent the value of ranking functions: a domain of affine ranking functions, and a domain of ordinal-valued ranking functions (which allows handling programs with unbounded non-determinism).
  • Functional Description: FuncTion is a research prototype static analyzer to analyze the termination and functional liveness properties of programs. It accepts programs in a small non-deterministic imperative language. It is also parameterized by a property: either termination, or a recurrence or a guarantee property (according to the classification by Manna and Pnueli of program properties). It then performs a backward static analysis that automatically infers sufficient conditions at the beginning of the program so that all executions satisfying the conditions also satisfy the property.
  • URL: http://www.di.ens.fr/~urban/FuncTion.html
  • Contacts: Caterina Urban, Antoine Miné
  • Participants: Antoine Miné, Caterina Urban

7.1.6 HOO

  • Name: Heap Abstraction for Open Objects
  • Functional Description:

    JSAna with HOO is a static analyzer for JavaScript programs. The primary component, HOO, which is designed to be reusable by itself, is an abstract domain for a dynamic language heap. A dynamic language heap consists of open, extensible objects linked together by pointers. Uniquely, HOO abstracts these extensible objects, where attribute/field names of objects may be unknown. Additionally, it contains features to keeping precise track of attribute name/value relationships as well as calling unknown functions through desynchronized separation.

    As a library, HOO is useful for any dynamic language static analysis. It is designed to allow abstractions for values to be easily swapped out for different abstractions, allowing it to be used for a wide-range of dynamic languages outside of JavaScript.

  • Contact: Arlen Cox
  • Participant: Arlen Cox

7.1.7 MemCAD

  • Name: The MemCAD static analyzer
  • Keywords: Static analysis, Abstraction
  • Functional Description: MemCAD is a static analyzer that focuses on memory abstraction. It takes as input C programs, and computes invariants on the data structures manipulated by the programs. It can also verify memory safety. It comprises several memory abstract domains, including a flat representation, and two graph abstractions with summaries based on inductive definitions of data-structures, such as lists and trees and several combination operators for memory abstract domains (hierarchical abstraction, reduced product). The purpose of this construction is to offer a great flexibility in the memory abstraction, so as to either make very efficient static analyses of relatively simple programs, or still quite efficient static analyses of very involved pieces of code. The implementation consists of over 30 000 lines of ML code, and relies on the ClangML front-end. The current implementation comes with over 300 small size test cases that are used as regression tests.
  • URL: http://www.di.ens.fr/~rival/memcad.html
  • Authors: Xavier Rival, Antoine Toubhans, Huisong Li, Liu Jiangchao, François Berenger, Pascal Sotin, Pippijn Van Steenhoven
  • Contacts: Xavier Rival, François Berenger, Huisong Li, Antoine Toubhans, Liu Jiangchao
  • Participants: Antoine Toubhans, François Berenger, Huisong Li, Xavier Rival

7.1.8 KAPPA

  • Name: A rule-based language for modeling interaction networks
  • Keywords: Systems Biology, Modeling, Static analysis, Simulation, Model reduction
  • Scientific Description: OpenKappa is a collection of tools to build, debug and run models of biological pathways. It contains a compiler for the Kappa Language, a static analyzer (for debugging models), a simulator, a compression tool for causal traces, and a model reduction tool.
  • Functional Description: Kappa is provided with the following tools: - a compiler - a stochastic simulator - a static analyzer - a trace compression algorithm - an ODE generator.
  • Release Contributions: On line UI, Simulation is based on a new data-structure (see ESOP 2017 ), New abstract domains are available in the static analyzer (see SASB 2016), Local traces (see TCBB 2018), Reasoning on polymers (see SASB 2018).
  • URL: http://www.kappalanguage.org/
  • Authors: Jean Krivine, Jérôme Feret, Kim-Quyen Ly, Pierre Boutillier
  • Contacts: Jérôme Feret, Jean Krivine
  • Participants: Jean Krivine, Jérôme Feret, Kim-Quyen Ly, Pierre Boutillier, Russ Harmer, Vincent Danos, Walter Fontana
  • Partners: ENS Lyon, Université Paris-Diderot, HARVARD Medical School

7.1.9 QUICr

  • Functional Description: QUICr is an OCaml library that implements a parametric abstract domain for sets. It is constructed as a functor that accepts any numeric abstract domain that can be adapted to the interface and produces an abstract domain for sets of numbers combined with numbers. It is relational, flexible, and tunable. It serves as a basis for future exploration of set abstraction.
  • Contact: Arlen Cox
  • Participant: Arlen Cox

7.1.10 Zarith

  • Functional Description:

    Zarith is a small (10K lines) OCaml library that implements arithmetic and logical operations over arbitrary-precision integers. It is based on the GNU MP library to efficiently implement arithmetic over big integers. Special care has been taken to ensure the efficiency of the library also for small integers: small integers are represented as Caml unboxed integers and use a specific C code path. Moreover, optimized assembly versions of small integer operations are provided for a few common architectures.

    Zarith is currently used in the Astrée analyzer to enable the sound analysis of programs featuring 64-bit (or larger) integers. It is also used in the Frama-C analyzer platform developed at CEA LIST and Inria Saclay.

  • URL: http://forge.ocamlcore.org/projects/zarith
  • Contacts: Antoine Miné, Xavier Leroy
  • Participants: Antoine Miné, Pascal Cuoq, Xavier Leroy

7.1.11 PYPPAI

  • Name: Pyro Probabilistic Program Analyzer
  • Keywords: Probability, Static analysis, Program verification, Abstraction
  • Functional Description:

    PYPPAI is a program analyzer to verify the correct semantic definition of probabilistic programs written in Pyro. At the moment, PYPPAI verifies consistency conditions between models and guides used in probabilistic inference programs.

    PYPPAI is written in OCaml and uses the pyml Python in OCaml library. It features a numerical abstract domain based on Apron, an abstract domain to represent zones in tensors, and dedicated abstract domains to describe distributions and states in probabilistic programs.

  • URL: https://github.com/wonyeol/static-analysis-for-support-match
  • Contact: Xavier Rival

8 New results

8.1 Shape Analysis

Survey on shape analysis

Participants: Bor-Yuh Evan Chang, Cezara Drăgoi, Roman Manevich, Noam Rinetzky, Xavier Rival.

Shape analysis has been gradually introduced over 25 years ago and has emerged into a key field in program analysis. It is concerned with the computation of precise semantic information about programs that manipulate complex data-structures, relying on dynamically allocated memory cells and destructive updates. Several large families of shape analysis have been developed and have evolved rather independently. For instance, we can cite shape analyses based on three-valued logics that rely on the selection of a family of specific predicates and predicate tables in a logics with “true”, “false” and “maybe” values. We can also cite shape analyses based on separation logic, whith conjoin the abstraction of separate memory regions with a spatial conjunction operator called separating conjunction. As a consequence, the field of shape analysis may appear overwhelming to newcomers and to potential users (many static analyses require information about memory data-structures and need to exploit results obtained by shape analysis).

To alleviate this, we have collaborated with experts in various forms of shape analysis on an ambitious survey paper project. This work spanned over several years and let to the publication of a 160 pages survey in Fall 2020 11.

8.2 Relational Static Analysis

Relational abstraction for memory properties

Participants: Hugo Illous, Matthieu Lemerre, Xavier Rival.

Static analyses aim at inferring semantic properties of programs. We can distinguish two important classes of static analyses: state analyses and relational analyses. While state analyses aim at computing an over-approximation of reachable states of programs, relational analyses aim at computing functional properties over the input-output states of programs. Several advantages of relational analyses are their ability to analyze incomplete programs, such as libraries or classes, but also to make the analysis modular, using input-output relations as composable summaries for procedures. In the case of numerical programs, several analyses have been proposed that utilize relational numerical abstract domains to describe relations. On the other hand, designing abstractions for relations over input-output memory states and taking shapes into account is challenging. We have proposed a set of novel logical connectives to describe such relations, which are inspired by separation logic. This logic can express that certain memory areas are unchanged, freshly allocated, or freed, or that only part of the memory was modified. Using these connectives, we have built an abstract domain and design a static analysis that over-approximates relations over memory states containing inductive structures. We implemented this analysis and evaluated it on a basic library of list manipulating functions.

This work was initially done as part of the PhD of Hugo Illous 31, and we have been working on the extension of this work and on its publication in a journal paper, which is currently accepted under revision, with publication date expteced for 2021.

Interprocedural Shape Analysis Using Separation Logic-based Transformer Summaries

Participants: Hugo Illous, Matthieu Lemerre, Xavier Rival.

Interprocedural static analysis focuses on the analysis of programs with functions, and traditionally relies on two main approaches: the first uses a state abstraction and computes over-approximations for sets of states in a finite collection of abstract contexts; the second abstracts the effect of each procedure using a relation.

Shape analyses aim at inferring semantic invariants related to the data-structures that programs manipulate. To achieve that, they typically abstract the set of reachable states, which implies that they fit nicely with the first approach to interprocedural analysis, but not to the second.

By contrast, abstractions for transformation relations between input states and output states not only provide a finer description of program executions but also enable the composition of the effect of program fragments so as to make the analysis modular. However, few logics can efficiently capture such transformation relations. In this work, we proposed to use connectors inspired by separation logic to describe memory state transformations and to represent procedure summaries. Based on this abstraction, we designed a top-down interprocedural analysis using shape transformation relations as procedure summaries. Finally, we report on implementation and evaluation.

This work was initially done as part of the PhD of Hugo Illous 31, and was published at SAS 2020 19.

8.3 Reduced product


Sharing Ghost Variables in a Collection of Abstract Domains


Participants: Marc Chevalier, Jérôme Feret.

In abstract interpretation, it is often necessary to be able to express complex properties while doing a precise analysis. A way to achieve that is to combine a collection of domains, each handling some kind of properties, using a reduced product. Separating domains allows an easier and more modular implementation, and eases soundness and termination proofs. This way, we can add a domain for any kind of property that is interesting. The reduced product, or an approximation of it, is in charge of refining abstract states, making the analysis precise.

In program verification, ghost variables can be used to ease proofs of properties by storing intermediate values that do not appear directly in the execution.

In 15, we propose a reduced product of abstract domains that allows domains to use ghost variables to ease the representation of their internal state. Domains must be totally agnostic with respect to other existing domains. In particular the handling of ghost variables must be entirely decentralized while still ensuring soundness and termination of the analysis.

8.4 Static Analysis of Probabilistic Programming Languages and Optimization Algorithms

Towards the verification of semantic assumptions required by probabilistic inference algorithms

Participants: Wonyeol Lee, Hangyeol Wu, Xavier Rival, Hongseok Yang.

Probabilistic programming is the idea of writing models from statistics and machine learning using program notations and reasoning about these models using generic inference engines. Recently its combination with deep learning has been explored intensely, which led to the development of so called deep probabilistic programming languages, such as Pyro, Edward and ProbTorch. At the core of this development lie inference engines based on stochastic variational inference algorithms. When asked to find information about the posterior distribution of a model written in such a language, these algorithms convert this posterior-inference query into an optimisation problem and solve it approximately by a form of gradient ascent or descent. We analysed one of the most fundamental and versatile variational inference algorithms, called score estimator or REINFORCE, using tools from denotational semantics and program analysis. We formally expressed what this algorithm does on models denoted by programs, and exposed implicit assumptions made by the algorithm on the models. The violation of these assumptions may lead to an undefined optimisation objective or the loss of convergence guarantee of the optimisation process. We then describe rules for proving these assumptions, which can be automated by static program analyses. Some of our rules use nontrivial facts from continuous mathematics, and let us replace requirements about integrals in the assumptions, such as integrability of functions defined in terms of programs’ denotations, by conditions involving differentiation or boundedness, which are much easier to prove automatically (and manually). Following our general methodology, we have developed a static program analysis for the Pyro programming language that aims at discharging the assumption about what we call model-guide support match. Our analysis is applied to the eight representative model-guide pairs from the Pyro webpage, which include sophisticated neural network models such as AIR. It found a bug in one of these cases, and revealed a non-standard use of an inference engine in another, and showed that the assumptions are met in the remaining six cases.

This work has been published at POPL 2020 13.

On correctness of automatic differentiation for non-differentiable functions

Participants: Wonyeol Lee, Hangyeol Wu, Xavier Rival, Hongseok Yang.

Differentiation lies at the core of many machine-learning algorithms, and is well-supported by popular autodiff systems, such as TensorFlow and PyTorch. Originally, these autodiff systems have been developed to compute derivatives of differentiable functions, but in practice, they are commonly applied to functions with non-differentiabilities. For instance, neural networks using ReLU define non-differentiable functions in general, but the gradients of losses involving those functions are computed using autodiff systems in practice. This status quo raises a natural question: are autodiff systems correct in any formal sense when they are applied to such non-differentiable functions? In this work, we provided a positive answer to this question. Using counterexamples, we first point out flaws in often-used informal arguments, such as: non-differentiabilities arising in deep learning do not cause any issues because they form a measure-zero set. We then investigate a class of functions, called PAP functions, that includes nearly all (possibly non-differentiable) functions in deep learning nowadays. For these PAP functions, we propose a new type of derivatives, called intensional derivatives, and prove that these derivatives always exist and coincide with standard derivatives for almost all inputs. We also show that these intensional derivatives are what most autodiff systems compute or try to compute essentially. In this way, we formally establish the correctness of autodiff systems applied to non-differentiable functions.

This work has been published in 20.

8.5 Static Analysis of Neural Networks


Perfectly Parallel Fairness Certification


Participants: Caterina Urban, Maria Christakis, Valentin Wüestholz, Fuyuan Zhang.

Recently, there is growing concern that machine-learning models, which currently assist or even automate decision making, reproduce, and in the worst case reinforce, bias of the training data. The development of tools and techniques for certifying fairness of these models or describing their biased behavior is, therefore, critical.

In 14, we propose a perfectly parallel static analysis for certifying causal fairness of feed-forward neural networks used for classification tasks. When certification succeeds, our approach provides definite guarantees, otherwise, it describes and quantifies the biased behavior. We design the analysis to be sound, in practice also exact, and configurable in terms of scalability and precision, thereby enabling pay-as-you-go certification. We implement our approach in an open-source tool and demonstrate its effectiveness on models trained with popular datasets.

8.6 Reductions between synchronous and asynchronous programming abstractions


Testing consensus implementations using communication closure


Participants: Cezara Drăgoi, Constantin Enea, Burcu Kulahcioglu Ozkan, Rupak Majumdar, Filip Niksic.

Large scale production distributed systems are difficult to design and test. Correctness must be ensured when processes run asynchronously, at arbitrary rates relative to each other, and in the presence of failures, e.g., process crashes or message losses. These conditions create a huge space of executions that is difficult to explore in a principled way. Current testing techniques focus on systematic or randomized exploration of all executions of an implementation while treating the implemented algorithms as black boxes. On the other hand, proofs of correctness of many of the underlying algorithms often exploit semantic properties that reduce reasoning about correctness to a subset of behaviors. For example, the communication-closure property, used in many proofs of distributed consensus algorithms, shows that every asynchronous execution of the algorithm is equivalent to a lossy synchronous execution, thus reducing the burden of proof to only that subset. In a lossy synchronous execution, processes execute in lock-step rounds, and messages are either received in the same round or lost forever—such executions form a small subset of all asynchronous ones.

In 17 we formulate the communication-closure hypothesis, which states that bugs in implementations of distributed consensus algorithms will already manifest in lossy synchronous executions and present a testing algorithm based on this hypothesis. We prioritize the search space based on a bound on the number of failures in the execution and the rate at which these failures are recovered. We show that a random testing algorithm based on sampling lossy synchronous executions can empirically find a number of bugs—including previously unknown ones—in production distributed systems such as Zookeeper, Cassandra, and Ratis, and also produce more understandable bug traces.

Programming at the edge of synchrony


Participants: Cezara Drăgoi, Josef Widder, Damien Zufferey.

Synchronization primitives for fault-tolerant distributed systems that ensure an effective and efficient cooperation among processes are an important challenge in the programming languages community. In 18 we present a new programming abstraction, ReSync, for implementing benign and Byzantine fault-tolerant protocols. ReSync has a new round structure that offers a simple abstraction for group communication, like it is customary in synchronous systems, but also allows messages to be received one by one, like in the asynchronous systems. This extension allows implementing network and algorithm-specific policies for the message reception, which is not possible in classic round models. The execution of ReSync programs is based on a new generic round switch protocol that generalizes the famous theoretical result about consensus in the presence of partial synchrony by of Dwork, Lynch, and Stockmeyer. We evaluate experimentally the performance of ReSync’s execution platform, by comparing consensus implementations in ReSync with LibPaxos3, etcd, and Bft-SMaRt, three consensus libraries tolerant to benign, resp. byzantine faults.

Proving the security of software-intensive embedded systems by abstract interpretation

Participants: Marc Chevalier.

Marc Chevalier's thesis 24 is dedicated to the analysis of low-level software, like operating systems, by abstract interpretation. Analyzing OSes is a crucial issue to guarantee the safety of software systems since they are the layer immediately above the hardware and that all applicative tasks rely on them. For critical applications, we want to prove that the OS does not crash, and that it ensures the isolation of programs, so that an untrusted program cannot disrupt a trusted one. The analysis of this kind of programs raises specific issues. This is because OSes must control hardware using instructions that are meaningless in ordinary programs. In addition, because hardware features are outside the scope of C, source code includes assembly blocks mixed with C code. These are the two main axes in this thesis: handling mixed C and assembly, and precise abstraction of instructions that are specific to low-level software. This work is motivated by the analysis of a case study emanating from an industrial partner, which required the implementation of proposed methods in the static analyzer Astrée. The first part is about the formalization of a language mixing simplified models of C and assembly, from syntax to semantics. This specification is crucial to define what is legal and what is a bug, while taking into account the intricacy of interactions of C and assembly, in terms of data flow and control flow. The second part is a short introduction to abstract interpretation focusing on what is useful thereafter. The third part proposes an abstraction of the semantics of mixed C and assembly. This is actually a series of parametric abstractions handling each aspect of the semantics. The fourth part is interested in the question of the abstraction of instructions specific to low-level software. Interest properties can easily be proven using ghost variables, but because of technical reasons, it is difficult to design a reduced product of abstract domains that allows a satisfactory handling of ghost variables. This part builds such a general framework with domains that allow us to solve our problem and many others. The final part details properties to prove in order to guarantee isolation of programs that have not been treated since they raise many complicated questions. We also give some suggestions to improve the product of domains with ghost variables introduced in the previous part, in terms of features and performances.

8.7 Modeling

Integrative model for TGF-β signalling and extracallelular matrix

Participants: Nathalie Theret, Jérôme Feret, Arran Hodgkinson, Pierre Boutillier, Pierre Vignet, Ovidiu Radulescu.

The extracellular matrix is the most important regulator of cell-cell communication within tissues. The extracellular matrix is a complex structure, made up of a wide variety of molecules including proteins, proteglycans and glycoaminoglycans. It contributes to cell signaling through the action of both its constituents and their proteolytic cleaved fragments called ma-tricryptins. In addition, the extracellular matrix acts as a "reservoir" of growth factors and cytokines and regulates their bioavailability at the cell surface. By controlling cell signaling inputs, the extracellular matrix plays a key role in regulating cell phenotype (differentiation, proliferation, migration, etc.). In this context, signaling networks associated with the polypeptide transforming growth factor TGF-β are unique since their activation are controlled by the extracellular matrix and TGF-β is a major regulator of ECM remodeling in return.

In 22, we provide feedbacks from two approaches to model to model the extra-cellular matrix: rule based-languages on the first hand, and mesoscale partial differential equations on the second hand.

Rate Equations for Graphs

Participants: Vincent Danos, Tobias Heindel, Ricardo Honorato-Zimmer, Sandro Stucki.

We combine ideas from: 1) graph transformation systems (GTSs) stemming from the theory of formal languages and concurrency, and 2) mean field approximations (MFAs), a collection of approximation techniques ubiquitous in the study of complex dynamics to build a framework which generates rate equations for stochastic GTSs and from which one can derive MFAs of any order (no longer limited to the humanly computable). The procedure for deriving rate equations and their approximations can be automated. An implementation and example models are available online at https://rhz.github.io/fragger. We apply our techniques and tools to derive an expression for the mean velocity of a two-legged walker protein on DNA.

Scaling up epidemiological models with rule-based modelling

Participants: Vincent Danos, William Waites, Matteo Cavaliere, David Manheim, Jasmina Panovska-Griffiths.

We investigate the use of rule-based modelling applied to topics in infectious diseases. Rule-based models generalise reaction-based models with reagents that have internal state and may be bound together to form complexes, as in chemistry. Rule-based models allow us to express a broad class of models for processes of interest in epidemiology that would not otherwise be feasible in compartmental models. This includes dynamics commonly found in compartmental models such as the spread of a virus from an infectious to a susceptible population, and more complex dynamics outside the typical scope of such models such as social behaviours and decision-making, testing capacity constraints, and tracing of people exposed to a virus but not yet symptomatic 28.

Quantum neural networks

Participants: Brian Coyle, Vincent Danos, Elham Kashefi, Daniel Mills.

We study an application of a class of quantum circuits known as Born machines to generative modelling. We show that the circuits encountered during gradient-based training cannot be efficiently sampled from classically up to multiplicative error in the worst case. Our gradient-based training methods use cost functions known as the Sinkhorn divergence and the Stein discrepancy, not previously used in the gradient-based training of quantum circuits, and we also introduce quantum kernels to generative modelling. We show that these methods outperform the previous standard method, which used maximum mean discrepancy (MMD) as a cost function, and achieve this with minimal overhead. Finally, we discuss the ability of the model to learn hard distributions 12.

Stochastic graph rewriting

Participants: Nicolas Behr, Vincent Danos, Ilias Garnier.

We develop a novel method to analyse the dynamics of stochastic rewriting systems evolving over finitary adhesive, extensive categories. Our formalism is based on the so-called rule algebra framework and demonstrates a relationship between the combinatorics of the rewriting rules (as encoded in the rule algebra) and the dynamics which these rules generate on observables (as encoded in the stochastic mechanics formalism). We introduce the concept of combinatorial conversion, whereby under certain technical conditions the evolution equation for (the exponential generating function of) the statistical moments of observables can be expressed as the action of certain differential operators on formal power series. This permits us to formulate the novel concept of moment bisimulation, where two dynamical systems are compared in terms of their evolution of sets of observables that are in bijection. In particular, we exhibit non-trivial examples of graphical rewriting systems that are moment bisimilar to certain discrete rewriting systems (such as branching processes or the larger class of stochastic chemical reaction systems). Our results point towards applications of a vast number of existing well-established exact and approximate analysis techniques developed for chemical reaction systems to the far richer class of general stochastic rewriting systems 10.

8.8 Smart contracts

Reversible and composable financial contracts

Participants: Vincent Danos, Jean Krivine, Julien Prat.

We have defined and studied a protocol for (intertemporal) reversible transactions organised in trade lines and demonstrated its soundness. We show that within our protocol, novel instruments for zero-collateral intermediation can be built. Soudness amounts to proving that 1) participants to the protocol can upper bound their costs statically, and 2) novel game-theoretic forms of confluence of the execution which guarantee that at any given time step (e.g. in block time) one player can unilaterally fold the trade line, if s/he so wishes. These results were summarised in a paper published in the proceedings of the Tokenomics 2020 conference 26.

9 Bilateral contracts and grants with industry

9.1 Bilateral contracts with industry

9.1.1 Follow up to the AnaStaSec project

  • Title: Analyse de propriété de sécurité
  • Type: Research contracts funded by AirBus France
  • Duration: March 2019 - August 2018 and November 2019 - March 2020
  • Inria contact: Jérôme Feret
  • Abstract: An emerging structure in our information processing-based society is the notion of trusted complex systems interacting via heterogeneous networks with an open, mostly untrusted world. This view characterises a wide variety of systems ranging from the information system of a company to the connected components of a private house, all of which have to be connected with the outside.

    The goal of these constracts is to analyse an application that is used to filter messages from higher-level security regions to lower-level ones in trusted complex systems. This application shall check that messages are well-formed and that they match with existing requests. Moreover, so as to limit potential flows of information, one shall prove that the internal state of buffers are reset between the processing of each packet.

    To certify these properties, the front-end of Astrée has been upgraded with new directives to specify the properties of interest, and the analysis has been tuned to improve the analysis : 1) ghost variables are used to record the value of buffers between each packet processing so that already existing relational domains can prove that they are restored to the correct value, and 2) data-partitioning strategies have been implemented to separate the different modes of usage.

9.1.2 Disco project with Tezos

  • Title: DISCO: Synchronous Abstractions for Blockchain Infrastructures
  • Type: Research contracts funded by Tezos
  • Duration: September 2020 - September 2023
  • Inria contact: Xavier Rival, Jérôme Feret
  • Abstract: The literature in distributed computing distinguishes two main classes of computational models: asynchronous models have better performance, whereas synchronous models provide stronger formal guarantees. Implementations of distributed systems must operate in asynchronous models of computation, where performance emerges from the load of the system. The correctness of asynchronous protocols is very hard to prove, due to the challenges of concurrency, faults, buffered message queues, and message loss, altering, and re-ordering by the network. In contrast, synchronous models are based on (communication- closed) rounds, and this structure greatly facilitates verification. There are no interleavings, and the cumulative size of reception buffers is bounded by the number of processes in the network.

    The goal of this project is to increase the confidence we have in blockchain systems. We propose to: (1) define a synchronous computational model for blockchain algorithms and build a domain-specific language appropriate for this synchronous computational model, (2) equip the domain-specific language with support for mechanized formal verification with a high degree of automation, and (3) prototypically implement a dedicated runtime for efficiently executing, within an asynchronous context, algorithms defined for a synchronous models, together with a formal correctness proof that certifies the correctness of the synchronous abstraction with respect to the asynchronous runtime.

9.1.3 Exploratory collaboration with Airbus on static analysis for machine learning

  • Title: Formal Methods for Artificial Intelligence: State of the Art
  • Type: Research contract funded by AirBus France
  • Duration: October 2020 - December 2020
  • Inria contact: Caterina Urban
  • Abstract: Artificial intelligence is a key enabler for the development of autonomous aircrafts. In order to use this technology in critical systems, strict safety guarantees are necessary for the trained machine learning models. The actual state of the art in artificial intelligence does not allow providing such guarantees and new methods are currently being developed. Among these, formal methods and notably static analysis by abstract interpretation appear to be the most promising for critical systems, in terms of soundness and scalability. Moreover, formal methods are actually intensively used for the verification of critical avionics software and well accepted by certification authorities. Nevertheless, as many research teams are developing multiple methods that fall under the formal methods umbrella, a need has emerged for Airbus to better understand this academics ecosystem. The goal of this contract is to carry out a thorough report on the state of the art in formal methods for artificial intelligence and discuss perspectives and expectations for possible worthwhile future research directions.

10 Partnerships and cooperations

10.1 International initiatives

10.1.1 Inria international partners

Informal international partners

Xavier Rival has a long standing collaboration with Bor-Yuh Evan Chang (University of Colorado, Boulder, USA), on the abstraction of symbolic properties and of complex memory data-structures.

Xavier Rival has a set up a collaboration with Hongseok Yang (KAIST, Daejeon, South Korea), on the verification of probabilistic programs such as programs built in the Pyro framework.

Xavier Rival has started a collaboration with Shinya Katsumata, Jérémy Dubut, and Ichiro Hasuo (NII, Tokyo, Japan) on the formalization of abstract domains.

Xavier Rival has been working with Kwangkeun Yi on the writing of a book that should serve as an introduction to the field of static analysis, for students and engineers.

10.2 International research visitors

10.2.1 Visits of international scientists

Xavier Rival has visited National Institute for Informatics, Tokyo, Japan in March 2020.

10.3 European initiatives

10.3.1 FP7 & H2020 Projects

  • Type: IDEAS
  • Instrument: ERC Proof of Concept Grant 2018
  • Objectif: Static Analysis for the VErification of Spreadsheets
  • Duration: January 2019 - June 2020
  • Coordinator: INRIA (France)
  • Inria contact: Xavier Rival
  • Abstract: Spreadsheet applications (such as Microsoft Excel + VBA) are heavily used in a wide range of application domains including engineering, finance, management, statistics and health. However, they do not ensure robustness properties, thus spreadsheet errors are common and potentially costly. According to estimates, the annual cost of spreadsheet errors is around 7 billion dollars. For instance, in 2013, a series of spreadsheet errors at JPMorgan incurred 6 billion dollars trading losses. Yet, expert reports estimate about 90 % of the spreadsheets contain errors. The MemCAD ERC StG project opened the way to novel formal analysis techniques for spreadsheet applications. We propose to leverage these results into a toolbox able to safely verify, optimize and maintain spreadsheets, so as to reduce the likelihood of spreadsheet disasters. This toolbox will be commercialized by the startup MatrixLead.

10.4 National initiatives

10.4.1 AnaStaSec

  • Title: Static Analysis for Security Properties
  • Type: ANR générique 2014
  • Defi: Société de l'information et de la communication
  • Instrument: ANR grant
  • Duration: January 2015 - September 2019
  • Coordinator: INRIA Paris-Rocquencourt (France)
  • Others partners: Airbus France (France), AMOSSYS (France), CEA LIST (France), INRIA Rennes-Bretagne Atlantique (France), TrustInSoft (France)
  • Inria contact: Jérôme Feret
  • See also: http://www.di.ens.fr/ feret/anastasec/
  • Abstract: An emerging structure in our information processing-based society is the notion of trusted complex systems interacting via heterogeneous networks with an open, mostly untrusted world. This view characterises a wide variety of systems ranging from the information system of a company to the connected components of a private house, all of which have to be connected with the outside.

    It is in particular the case for some aircraft-embedded computer systems, which communicate with the ground through untrusted communication media. Besides, the increasing demand for new capabilities, such as enhanced on-board connectivity, e.g. using mobile devices, together with the need for cost reduction, leads to more integrated and interconnected systems. For instance, modern aircrafts embed a large number of computer systems, from safety-critical cockpit avionics to passenger entertainment. Some systems meet both safety and security requirements. Despite thorough segregation of subsystems and networks, some shared communication resources raise the concern of possible intrusions.

    Some techniques have been developed and still need to be investigated to ensure security and confidentiality properties of such systems. Moreover, most of them are model-based techniques operating only at architectural level and provide no guarantee on the actual implementations. However, most security incidents are due to attackers exploiting subtle implementation-level software vulnerabilities. Systems should therefore be analyzed at software level as well (i.e. source or executable code), in order to provide formal assurance that security properties indeed hold for real systems.

    Because of the size of such systems, and considering that they are evolving entities, the only economically viable alternative is to perform automatic analyses. Such analyses of security and confidentiality properties have never been achieved on large-scale systems where security properties interact with other software properties, and even the mapping between high-level models of the systems and the large software base implementing them has never been done and represents a great challenge. The goal of this project is to develop the new concepts and technologies necessary to meet such a challenge.

    The project AnaStaSec project will allow for the formal verification of security properties of software-intensive embedded systems, using automatic static analysis techniques at different levels of representation: models, source and binary codes. Among expected outcomes of the project will be a set of prototype tools, able to deal with realistic large systems and the elaboration of industrial security evaluation processes, based on static analysis.

10.4.2 DCore

  • Title: DCore - Causal Debugging for Concurrent Systems
  • Type: ANR générique 2018
  • Defi: Société de l'information et de la communication
  • Instrument: ANR grant
  • Duration: March 2019 - February 2023
  • Coordinator: INRIA Grenoble - Rhône-Alpes (France)
  • Others partners: IRIF (France), Inria Paris (France)
  • Inria contact: Jérôme Feret
  • See also: https://project.inria.fr/dcore/
  • Abstract: As software takes over more and more functionalities in embedded and safety-critical systems, bugs may endanger the safety of human beings and of the environment, or entail heavy financial losses. In spite of the development of verification and testing techniques, debugging still plays a crucial part in the arsenal of the software developer. Unfortunately, usual debugging techniques do not scale to large concurrent and distributed systems: they fail to provide precise and efficient means to inspect and analyze large concurrent executions; they do not provide means to automatically reveal software faults that constitute actual causes for errors; and they do not provide succinct and relevant explanations linking causes (software bugs) to their effects (errors observed during execution).

    The overall objective of the project is to develop a semantically well-founded, novel form of concurrent debugging, which we call "causal debugging”, that aims to alleviate the deficiencies of current debugging techniques for large concurrent software systems.

    Briefly, the causal debugging technology developed by the DCore project will comprise and integrate two main novel engines:

    1. A reversible execution engine that allows programmers to backtrack and replay a concurrent or distributed program execution, in a way that is both precise and efficient (only the exact threads involved by a return to a target anterior or posterior program state are impacted);
    2. a causal analysis engine that allows programmers to analyze concurrent executions, by asking questions of the form "what caused the violation of this program property?”, and that allows for the precise and efficient investigation of past and potential program executions.

    The project will build its causal debugging technology on results obtained by members of the team, as part of the past ANR project REVER, on the causal semantics of concurrent languages, and the semantics of concurrent reversible languages, as well as on recent works by members of the project on abstract interpretation, causal explanations and counterfactual causal analysis.

    The project primarily targets multithreaded, multicore and multiprocessor software systems, and functional software errors, that is errors that arise in concurrent executions because of faults (bugs) in software that prevents it to meet its intended function. Distributed systems, which can be impacted by network failures and remote site failures are not an immediate target for DCore, although the technology developed by the project should constitute an important contribution towards full-fledged distributed debugging. Likewise, we do not target performance or security errors, which come with specific issues and require different levels of instrumentation, although the DCore technology should prove a key contribution in these areas as well.

10.4.3 REPAS

The project REPAS, Reliable and Privacy-Aware Software Systems via Bisimulation Metrics (coordination Catuscia Palamidessi, INRIA Saclay), aims at investigating quantitative notions and tools for proving program correctness and protecting privacy, focusing on bisimulation metrics, the natural extension of bisimulation on quantitative systems. A key application is to develop mechanisms to protect the privacy of users when their location traces are collected. Partners: Inria (Comete, Focus), ENS Cachan, ENS Lyon, University of Bologna.

10.4.4 SAFTA

  • Title: SAFTA Static Analysis for Fault-Tolerant distributed Algorithms.
  • Type: ANR JCJC 2018
  • Duration: February 2018 - August 2022
  • Coordinator: Cezara Drăgoi, CR Inria
  • Abstract: Fault-tolerant distributed data structures are at the core distributed systems. Due to the multiple sources of non-determinism, their development is challenging. The project aims to increase the confidence we have in distributed implementations of data structures. We think that the difficulty does not only come from the algorithms but from the way we think about distributed systems. In this project we investigate partially synchronous communication-closed round based programming abstractions that reduce the number of interleavings, simplifying the reasoning about distributed systems and their proof arguments. We use partial synchrony to define reduction theorems from asynchronous semantics to partially synchronous ones, enabling the transfer of proofs from the synchronous world to the asynchronous one. Moreover, we define a domain specific language, that allows the programmer to focus on the algorithm task, it compiles into efficient asynchronous code, and it is equipped with automated verification engines.

10.4.5 VeriAMOS

  • Title: Verification of Abstract Machines for Operating Systems
  • Type: ANR générique 2018
  • Defi: Société de l'information et de la communication
  • Instrument: ANR grant
  • Duration: January 2019 - December 2022
  • Coordinator: INRIA Paris (France)
  • Others partners: LIP6 (France), IRISA (France), UGA (France)
  • Inria contact: Xavier Rival
  • Abstract: Operating System (OS) programming is notoriously difficult and error prone. Moreover, OS bugs can have a serious impact on the functioning of computer systems. Yet, the verification of OSes is still mostly an open problem, and has only been done using user-assisted approaches that require a huge amount of human intervention. The VeriAMOS proposal relies on a novel approach to automatically and fully verifying OS services, that combines Domain Specific Languages (DSLs) and automatic static analysis. In this approach, DSLs provide language abstraction and let users express complex policies in high-level simple code. This code is later compiled into low level C code, to be executed on an abstract machine. Last, the automatic static analysis verifies structural and robustness properties on the abstract machine and generated code. We will apply this approach to the automatic, full verification of input/output schedulers for modern supports like SSDs.

11 Dissemination

11.1 Promoting scientific activities

11.1.1 Scientific events: organisation

General chair, scientific chair
  • Jérôme Feret is a guest member of the Steering Committee of the Conference on Computational Methods in Systems Biology (CMSB).
  • Jérôme Feret is a member of the Steering Committee of the Workshop on Static Analysis and Systems Biology (SASB).
  • Xavier Rival is a member of the Steering Committee of the Static Analysis Symposium (SAS).
  • Xavier Rival is a member of the Steering Committee of the Workshop on Tools for Automatic Program Analysis (TAPAS).
  • Caterina Urban is a member of the Executive Board of ETAPS (European Joint Conferences on Theory & Practice of Software).
  • Vincent Danos is a member of the Steering Committee of the International Conference on Blockchain Economics, Security and Protocols (Tokenomics).
Member of the organizing committees
  • Caterina Urban is serving as Chair of the Award Committee of the ETAPS Doctoral Dissertation Award 2021.
  • Caterina Urban is organizing the 7th Verification Mentoring Workshop @CAV 2021.
  • Caterina Urban is organizing the 7th Logic Mentoring Workshop @LICS 2022.

11.1.2 Scientific events: selection

Chair of conference program committees

Caterina Urban is serving as Chair of SOAP 2021 (Workshop on the State of the Art in Program Analysis).

Member of the conference program committees
  • Jérôme Feret is serving as a Member of the Program Committee of CMSB 2020 (Conference on Computational Methods in Systems Biology) and chaired a session.
  • Jérôme Feret is serving as a Member of the Program Committee of SAS 2020 (Static Analysis Symposium) and chaired a session.
  • Jérôme Feret is serving as a Member of the Program Committee of HSB 2020 (Workshop on Hybrid Systems Biology).
  • Xavier Rival is serving as a Member of the Program Committee of POPL 2020 (Symposium on Principles Of Programming Languages) and chaired a session.
  • Xavier Rival served as a Member of the Program Committee of SAS 2020 (Static Analysis Symposium) and chaired a session.
  • Xavier Rival is serving as a Member of the Program Committee of SAS 2021 (Static Analysis Symposium).
  • Xavier Rival served as a Member of the Program Committee of VMCAI 2021 (Static Analysis Symposium) and chaired a session.
  • Caterina Urban served as a member of the Program Committee of VMCAI 2020 (Verification, Model Checking, and Abstract Interpretation).
  • Caterina Urban served as a member of the Program Committee of ESOP 2020 (European Symposium on Programming).
  • Caterina Urban served as a member of the Program Committee of CAV 2020 (Computer Aided Verification) and chaired a session.
  • Caterina Urban served as a member of the Program Committee of SOAP 2020 (Workshop on the State Of the Art in Program Analysis).
  • Caterina Urban served as a member of the Program Committee of VSTTE 2020 (Verified Software. Theories, Tools, and Experiments).
  • Caterina Urban served as a member of the Program Committee of SAS 2020 (Static Analysis Symposium) and chaired a session.
  • Caterina Urban served as a member of the Committee of the SPLASH 2020 Student Research Competition.
  • Caterina Urban served as a member of the Program Committee of FAccT 2021 (Fairness, Accountability, and Transparency)
  • Caterina Urban is serving as a member of the Program Committee of NFM 2021 (NASA Formal Methods)
  • Caterina Urban is serving as a member of the Program Committee of CAV 2021 (Computer Aided Verification)
  • Caterina Urban is serving as a member of the Program Committee of SBLP 2021 (Brazilian Symposium on Programming Languages)
  • Caterina Urban is serving as a member of the Program Committee of POPL 2022 (Symposium on Principles of Programming Languages)

Jérôme Feret served as Reviewer for CAV 2020 (Conference on Computer-Aided Verification) and MFCS 2020 (Symposium on Mathematical Foundations of Computer Science). Caterina Urban served as Reviewer for POPL 2020 (Symposium on Principles of Programming Languages). Vincent Danos served as a reviewer for ICBC 2021 (International Conference on Blockchain and Cryptocurrency).

11.1.3 Journal

Member of the editorial boards
  • Jérôme Feret is in the editorial board of Open Journal of Modelling and Simulation.
  • Jérôme Feret is in the editorial board of Frontiers in Genetics.
  • Vincent Danos is a member of the editorial board of Mathematical Structures in Computer Science.
  • Vincent Danos is a member of the editorial board of Transactions in Computational Systems Biology
  • Vincent Danos was invited to serve as a member of the editorial board of Life, June 2020.
Reviewer - reviewing activities
  • Jérôme Feret serves as a reviewer for Theoretical Computer Science.
  • Jérôme Feret serves as a reviewer for BioInformatics.

11.1.4 Invited talks

  • Xavier Rival was invited to give a talk on “Construction of modular abstract domains” at University of Lille on the 12th of February 2020.
  • Xavier Rival was invited to give a talk on “Type verification of spreadsheet applications” on the 3rd of march 2020.
  • Xavier Rival was invited to give a talk on “Relational shape analysis” at Tel Aviv University (Israel) on the 21st of June 2020.
  • Caterina Urban was invited to give a talk on “Perfectly Parallel Fairness Certification of Neural Networks” at Thales Research & Technology (remote) on May 15th, 2020.
  • Caterina Urban was invited to give a talk on “Perfectly Parallel Fairness Certification of Neural Networks” at Tel Aviv University (Israel, remote) on May 24th, 2020.
  • Caterina Urban was invited to give a talk on “Perfectly Parallel Fairness Certification of Neural Networks” at IRIF (remote) on June 3rd, 2020.
  • Caterina Urban was invited to give a talk on “Perfectly Parallel Fairness Certification of Neural Networks” at Inria Rennes (remote) on June 18th, 2020.
  • Caterina Urban was invited to give a talk on “A Static Analyzer for Data Science Software” at DSV 2020 (Workshop on Democratizing Software Verification), USA (remote) on July 20th, 2020.
  • Caterina Urban was invited to give a talk on “Static Analysis for Data Science” at INSERM (remote) on November 2nd, 2020.
  • Vincent Danos was invited to give a talk on “Decentralised Finance and capital efficiency” at the Paris Blockchain Week Summit, December 9, 2020.
  • Vincent Danos was invited to give a talk on “Competition mechanisms in Decentralised Finance” at the Labchain Think Tank (Caisse de dépôts), December 4, 2020.
  • Vincent Danos was invited to give a talk on “Automated market-makers” at the Chalmers University seminar on Runtime Verification, November 17, 2020.
  • Vincent Danos was invited to give a talk on “Automated market-makers” at the TU Berlin seminar on Software Engineering, October 20, 2020.

11.1.5 Leadership within the scientific community

  • Xavier Rival is a member of the IFIP Working Group 2.4 on Software implementation technology.

11.1.6 Research administration

  • Jérôme Feret and Xavier Rival are members of the Laboratory Council of DIENS.
  • Jérôme Feret is member of the PhD Review Committee (CSD) of Inria Paris.
  • Until August 2020, Jérôme Feret is deputy dean of study of the Department of Computer Science of École normale supérieure.
  • Since September 2020, Jérôme Feret is dean of study of the Department of Computer Science of École normale supérieure.

11.2 Teaching - Supervision - Juries

11.2.1 Teaching

  • Licence:
    • Marc Chevalier, Mathematics, 40h, L1, FDV Bachelor program (Frontiers in Life Sciences (FdV)), Université Paris-Descartes, France.
    • Jérôme Feret and Xavier Rival (lectures), and Marc Chevalier (tutorials), “Semantics and Application to Verification”, 36h, L3, at École Normale Supérieure, France.
    • Xavier Rival, “Functional Programming”, 21h, L3, Bachelor Programme at at École Polytechnique, France.
  • Master:
    • Xavier Rival, “Introduction to Static Analysis”, 12h, Internet of Things Master (retraining curriculum, EXED), France.
    • Jérôme Feret, Antoine Miné, Xavier Rival, and Caterina Urban, “Abstract Interpretation: application to verification and static analysis”, 72h, M2. Parisian Master of Research in Computer Science (MPRI), France.
    • Vincent Danos and Jérôme Feret (with Jean Krivine), Rule-based Modelling, 24h, M1. Interdisciplinary Approaches to Life Science (AIV), Master Program, Université Paris-Descartes, France.

11.2.2 Supervision

  • PhD defended: Marc Chevalier, Static analysis of Security Properties in Critical Embedded Software, started in 2017 and supervised by Jérôme Feret
  • PhD in progress: Albin Salazar, Formal derivation of discrete models with separated time-scales, started in 2019 and supervised by Jérôme Feret
  • PhD in progress: Olivier Nicole, Verification of micro-kernels, started in 2018 and supervised by Xavier Rival and Matthieu Lemerre (CEA)
  • PhD in progress: Josselin Giet, Functional verification of components of operating systems by static analysis, started in 2020 and supervised by Xavier Rival and Gilles Muller (INRIA Paris, Project team Whisper).
  • PhD in progress: Ignacio Tiraboshi, Static analysis for security properties on IoT applications, started in 2020 and supervised by Xavier Rival and Tamara Rezk (INRIA Sophia, Project team Indes).
  • PhD in progress: Denis Mazzucato, Static Analysis by Abstract Interpretation of Machine-Learned Software, started in 2020 and supervised by Caterina Urban

11.2.3 Juries

  • Xavier Rival served as a member of the Review Committee for the PhD of Vincenzo Arceri at University of Verona (Defense: March 2020).
  • Xavier Rival served as a reviewer and as a member of jury for the PhD defense of Chaoqiang Deng at Courant Mathematical Institute of New York University (Defense: August 2020).
  • Caterina Urban served as a reviewer for the PhD defense of Marco Zanella at the University of Padova, Italy (Defense: March/April 2021).

11.3 Popularization

11.3.1 Internal or external Inria responsibilities

  • Cezara Drăgoi and Xavier Rival are elected members of the INRIA Commision of Evaluation
  • Jérôme Feret is a member of the jury of the ISIF - Gilles Kahn PhD Award.
  • Xavier Rival is member of the “Bureau du comité des projets”.
  • Xavier Rival served in the “admissibility” jury for INRIA researcher positions (CRCN) for the center of “Nancy Grand Est” and for the national campaign in 2020.
  • Xavier Rival served in the “admission” jury for INRIA researcher positions (CRCN) for all centers in 2020.
  • Caterina Urban is serving in the INRIA Commission Emplois Scientifique in 2021.

11.3.2 Articles and contents

  • Cezara Drăgoi and Xavier Rival co-authored with Bor-Yuh Evan Chang, Noam Rinetzky and Roman Manevich a survery on shape analysis, which was published in Fall 2020 11.
  • Jérôme Feret authored a book chapter on static analysis of rule-based models 27.
  • Xavier Rival has been working with Kwangkeun Yi on the writing of a book that should serve as an introduction to the field of static analysis, for students and engineers, and this book is expected to be released by MIT Press in January 2020 21.

12 Scientific production

12.1 Major publications

  • 1 inproceedings JulienJ. Bertrane, PatrickP. Cousot, RadhiaR. Cousot, JérômeJ. Feret, LaurentL. Mauborgne, AntoineA. Miné and XavierX. Rival. Static Analysis and Verification of Aerospace Software by Abstract Interpretation Proceedings of the American Institute of Aeronautics and Astronautics (AIAA Infotech@Aerospace 2010) Atlanta, Georgia, USA American Institute of Aeronautics and Astronautics 2010
  • 2 inproceedingsBrunoB. Blanchet, PatrickP. Cousot, RadhiaR. Cousot, JérômeJ. Feret, LaurentL. Mauborgne, AntoineA. Miné, DavidD. Monniaux and XavierX. Rival. A Static Analyzer for Large Safety-Critical SoftwareProceedings of the ACM SIGPLAN 2003 Conference on Programming Language Design and Implementation (PLDI'03)ACM PressJune 7--14 2003, 196--207
  • 3 inproceedingsAhmedA. Bouajjani, CezaraC. Dragoi, ConstantinC. Enea and MihaelaM. Sighireanu. On inter-procedural analysis of programs with lists and dataProceedings of the 32nd ACM SIGPLAN Conference on Programming Language Design and Implementation, PLDI 2011, San Jose, CA, USA, June 4-8, 20112011, 578--589URL: http://doi.acm.org/10.1145/1993498.1993566
  • 4 articlePatrickP. Cousot. Constructive Design of a Hierarchy of Semantics of a Transition System by Abstract InterpretationTheoretical Computer Science2771--22002, 47--103
  • 5 article JérômeJ. Feret, VincentV. Danos, JeanJ. Krivine, RussR. Harmer and WalterW. Fontana. Internal coarse-graining of molecular systems Proceeding of the national academy of sciences 106 16 Apr 2009
  • 6 inproceedingsLaurentL. Mauborgne and XavierX. Rival. Trace Partitioning in Abstract Interpretation Based Static AnalyzersProceedings of the 14th European Symposium on Programming (ESOP'05)3444Lecture Notes in Computer ScienceSpringer-Verlag2005, 5--20
  • 7 articleAntoineA. Miné. The Octagon Abstract DomainHigher-Order and Symbolic Computation192006, 31--100
  • 8 inproceedingsXavierX. Rival. Symbolic Transfer Functions-based Approaches to Certified CompilationConference Record of the 31st Annual ACM SIGPLAN\discretionary--SIGACT Symposium on Principles of Programming LanguagesACM Press, New York, United States2004, 1--13

12.2 Publications of the year

International journals

  • 9 article FrancescaF. Angileri, SébastienS. Legare, AntonellaA. Marino Gammazza, EverlyE. Conway de Macario, AlbertoA. JL Macario and FrancescoF. Cappello. Molecular mimicry may explain multi-organ damage in COVID-19 Autoimmunity Reviews 19 8 August 2020
  • 10 article NicolasN. Behr, VincentV. Danos and IliasI. Garnier. Combinatorial Conversion and Moment Bisimulation for Stochastic Rewriting Systems Logical Methods in Computer Science July 2020
  • 11 articleBor-Yuh EvanB.-Y. Chang, CezaraC. Dragoi, RomanR. Manevich, NoamN. Rinetzky and XavierX. Rival. Shape AnalysisFoundations and Trends in Programming Languages61–22020, 1-158
  • 12 articleBrianB. Coyle, DanielD. Mills, VincentV. Danos and ElhamE. Kashefi. The Born supremacy: quantum advantage and training of an Ising Born machinenpj Quantum Information61July 2020, 60
  • 13 article WonyeolW. Lee, HangyeolH. Yu, XavierX. Rival and HongseokH. Yang. Towards Verified Stochastic Variational Inference for Probabilistic Programs Proceedings of the ACM on Programming Languages 16 2020
  • 14 articleCaterinaC. Urban, MariaM. Christakis, ValentinV. Wüstholz and FuyuanF. Zhang. Perfectly Parallel Fairness Certification of Neural NetworksProceedings of the ACM on Programming Languages4OOPSLANovember 2020, 1-30

International peer-reviewed conferences

  • 15 inproceedings MarcM. Chevalier and JérômeJ. Feret. Sharing Ghost Variables in a Collection of Abstract Domains VMCAI 2020 - 21st International Conference on Verification, Model Checking, and Abstract Interpretation Proceedings of the 21st International Conference on Verification, Model Checking, and Abstract Interpretation (VMCAI 2020) New Orleans, LA, United States January 2020
  • 16 inproceedingsVincentV. Danos, TobiasT. Heindel, RicardoR. Honorato-Zimmer and SandroS. Stucki. Rate Equations for GraphsCMSB 2020 - 18th International Conference Computational Methods in Systems BiologyKonstanz / Virtual, GermanySeptember 2020, 3-26
  • 17 inproceedings CezaraC. Dragoi, ConstantinC. Enea, Burcu KulahciogluB. Ozkan, RupakR. Majumdar and FilipF. Niksic. Testing consensus implementations using communication closure Proc. ACM Program. Lang. 4(OOPSLA): 210:1-210:29 (2020) Chiccago, United States October 2021
  • 18 inproceedings CezaraC. Dragoi, JosefJ. Widder and DamienD. Zufferey. Programming at the edge of synchrony Proc. ACM Program. Lang. 4(OOPSLA): 213:1-213:30 (2020) Chicago, United States October 2020
  • 19 inproceedings HugoH. Illous, MatthieuM. Lemerre and XavierX. Rival. Interprocedural Shape Analysis Using Separation Logic-based Transformer Summaries SAS 2020 - 27th Static Analysis Symposium Chicago / Virtual, United States November 2020
  • 20 inproceedings WonyeolW. Lee, HangyeolH. Yu, XavierX. Rival and HongseokH. Yang. On Correctness of Automatic Differentiation for Non-Differentiable Functions NeurIPS 2020 - 34th Conference on Neural Information Processing Systems Vancouver / Virtual, Canada December 2020

Scientific books

Scientific book chapters

Edition (books, proceedings, special issue of a journal)

  • 23 proceedings V. Danos M. Herlihy M. Potop-Butucaru J. Prat S. Tucci-Piergiovanni International Conference on Blockchain Economics, Security and Protocols (Tokenomics 2019) 71 OpenAccess Series in Informatics (OASIcs) Paris, France March 2020

Doctoral dissertations and habilitation theses

  • 24 thesis MarcM. Chevalier. Proving the security of software-intensive embedded systems by abstract interpretation ENS Paris; PSL University November 2020

Reports & preprints

12.3 Cited publications

  • 29 articlePatrickP. Cousot. Constructive design of a hierarchy of semantics of a transition system by abstract interpretationElectr. Notes Theor. Comput. Sci.61997, 77--102URL: http://dx.doi.org/10.1016/S1571-0661(05)80168-9
  • 30 inproceedingsPatrickP. Cousot and RadhiaR. Cousot. Abstract interpretation: a unified lattice model for static analysis of programs by construction or approximation of fixpointsConference Record of the Fourth Annual ACM SIGPLAN-SIGACT Symposium on Principles of Programming LanguagesACM Press, New York, United States1977, 238--252
  • 31 phdthesis HugoH. Illous. Abstract Heap Relations for a Compositional Shape Analysis Ecole Normale Supérieure 4 2019
  • 32 inproceedingsCaterinaC. Urban. Static Analysis of Data Science SoftwareSAS 2019 - 26th Static Analysis SymposiumPorto, PortugalSpringer10 2019, 17-23