Section: Research Program

Scientific background

Model-driven engineering

Model-Driven Engineering (MDE) aims at reducing the accidental complexity associated with developing complex software-intensive systems (e.g., use of abstractions of the problem space rather than abstractions of the solution space) [132] . It provides DIVERSE with solid foundations to specify, analyze and reason about the different forms of diversity that occur through the development lifecycle. A primary source of accidental complexity is the wide gap between the concepts used by domain experts and the low-level abstractions provided by general-purpose programming languages [102] . MDE approaches address this problem through modeling techniques that support separation of concerns and automated generation of major system artifacts from models (e.g., test cases, implementations, deployment and configuration scripts). In MDE, a model describes an aspect of a system and is typically created or derived for specific development purposes [81] . Separation of concerns is supported through the use of different modeling languages, each providing constructs based on abstractions that are specific to an aspect of a system. MDE technologies also provide support for manipulating models, for example, support for querying, slicing, transforming, merging, and analyzing (including executing) models. Modeling languages are thus at the core of MDE, which participates to the development of a sound Software Language Engineering (See http://planet-sl.org ), including an unified typing theory that integrate models as first class entities [135] .

Incorporating domain-specific concepts and high-quality development experience into MDE technologies can significantly improve developer productivity and system quality. Since the late nineties, this realization has led to work on MDE language workbenches that support the development of domain-specific modeling languages (DSMLs) and associated tools (e.g., model editors and code generators). A DSML provides a bridge between the field in which domain experts work and the implementation (programming) field. Domains in which DSMLs have been developed and used include, among others, automotive, avionics, and the emerging cyber-physical systems. A study performed by Hutchinson et al. [108] provides some indications that DSMLs can pave the way for wider industrial adoption of MDE.

More recently, the emergence of new classes of systems that are complex and operate in heterogeneous and rapidly changing environments raises new challenges for the software engineering community. These systems must be adaptable, flexible, reconfigurable and, increasingly, self-managing. Such characteristics make systems more prone to failure when running and thus the development and study of appropriate mechanisms for continuous design and run-time validation and monitoring are needed. In the MDE community, research is focused primarily on using models at design, implementation, and deployment stages of development. This work has been highly productive, with several techniques now entering a commercialization phase. As software systems are becoming more and more dynamic, the use of model-driven techniques for validating and monitoring run-time behavior is extremely promising [116] .

Variability modeling

While the basic vision underlying Software Product Lines (SPL) can probably be traced back to David Parnas seminal article  [125] on the Design and Development of Program Families, it is only quite recently that SPLs are emerging as a paradigm shift towards modeling and developing software system families rather than individual systems  [122] . SPL engineering embraces the ideas of mass customization and software reuse. It focuses on the means of efficiently producing and maintaining multiple related software products, exploiting what they have in common and managing what varies among them.

Several definitions of the software product line concept can be found in the research literature. Clements et al. define it as a set of software-intensive systems sharing a common, managed set of features that satisfy the specific needs of a particular market segment or mission and are developed from a common set of core assets in a prescribed way [121] . Bosch provides a different definition [87] : A SPL consists of a product line architecture and a set of reusable components designed for incorporation into the product line architecture. In addition, the PL consists of the software products developed using the mentioned reusable assets. In spite of the similarities, these definitions provide different perspectives of the concept: market-driven, as seen by Clements et al., and technology-oriented for Bosch.

SPL engineering is a process focusing on capturing the commonalities (assumptions true for each family member) and variability (assumptions about how individual family members differ) between several software products [96] . Instead of describing a single software system, a SPL model describes a set of products in the same domain. This is accomplished by distinguishing between elements common to all SPL members, and those that may vary from one product to another. Reuse of core assets, which form the basis of the product line, is key to productivity and quality gains. These core assets extend beyond simple code reuse and may include the architecture, software components, domain models, requirements statements, documentation, test plans or test cases.

The SPL engineering process consists of two major steps:

  1. Domain Engineering, or development for reuse, focuses on core assets development.

  2. Application Engineering, or development with reuse, addresses the development of the final products using core assets and following customer requirements.

Central to both processes is the management of variability across the product line  [104] . In common language use, the term variability refers to the ability or the tendency to change. Variability management is thus seen as the key feature that distinguishes SPL engineering from other software development approaches [88] . Variability management is thus growingly seen as the cornerstone of SPL development, covering the entire development life cycle, from requirements elicitation  [137] to product derivation  [142] to product testing  [120] , [119] .

Halmans et al. [104] distinguish between essential and technical variability, especially at requirements level. Essential variability corresponds to the customer's viewpoint, defining what to implement, while technical variability relates to product family engineering, defining how to implement it. A classification based on the dimensions of variability is proposed by Pohl et al. [127] : beyond variability in time (existence of different versions of an artifact that are valid at different times) and variability in space (existence of an artifact in different shapes at the same time) Pohl et al. claim that variability is important to different stakeholders and thus has different levels of visibility: external variability is visible to the customers while internal variability, that of domain artifacts, is hidden from them. Other classification proposals come from Meekel et al. [114] (feature, hardware platform, performances and attributes variability) or Bass et al. [79] who discuss about variability at the architectural level.

Central to the modeling of variability is the notion of feature, originally defined by Kang et al. as: a prominent or distinctive user-visible aspect, quality or characteristic of a software system or systems  [110] . Based on this notion of feature, they proposed to use a feature model to model the variability in a SPL. A feature model consists of a feature diagram and other associated information: constraints and dependency rules. Feature diagrams provide a graphical tree-like notation depicting the hierarchical organization of high level product functionalities represented as features. The root of the tree refers to the complete system and is progressively decomposed into more refined features (tree nodes). Relations between nodes (features) are materialized by decomposition edges and textual constraints. Variability can be expressed in several ways. Presence or absence of a feature from a product is modeled using mandatory or optional features. Features are graphically represented as rectangles while some graphical elements (e.g., unfilled circle) are used to describe the variability (e.g., a feature may be optional).

Features can be organized into feature groups. Boolean operators exclusive alternative (XOR), inclusive alternative (OR) or inclusive (AND) are used to select one, several or all the features from a feature group. Dependencies between features can be modeled using textual constraints: requires (presence of a feature requires the presence of another), mutex (presence of a feature automatically excludes another). Feature attributes can be also used for modeling quantitative (e.g., numerical) information. Constraints over attributes and features can be specified as well.

Modeling variability allows an organization to capture and select which version of which variant of any particular aspect is wanted in the system  [88] . To implement it cheaply, quickly and safely, redoing by hand the tedious weaving of every aspect is not an option: some form of automation is needed to leverage the modeling of variability  [83] , [98] . Model Driven Engineering (MDE) makes it possible to automate this weaving process  [109] . This requires that models are no longer informal, and that the weaving process is itself described as a program (which is as a matter of facts an executable meta-model  [117] ) manipulating these models to produce for instance a detailed design that can ultimately be transformed to code, or to test suites  [126] , or other software artifacts.

Component-based software development

Component-based software development [136] aims at providing reliable software architectures with a low cost of design. Components are now used routinely in many domains of software system designs: distributed systems, user interaction, product lines, embedded systems, etc. With respect to more traditional software artifacts (e.g., object oriented architectures), modern component models have the following distinctive features  [97] : description of requirements on services required from the other components; indirect connections between components thanks to ports and connectors constructs  [112] ; hierarchical definition of components (assemblies of components can define new component types); connectors supporting various communication semantics  [91] ; quantitative properties on the services  [86] .

In recent years component-based architectures have evolved from static designs to dynamic, adaptive designs (e.g., SOFA  [91] , Palladio  [84] , Frascati  [118] ). Processes for building a system using a statically designed architecture are made of the following sequential lifecycle stages: requirements, modeling, implementation, packaging, deployment, system launch, system execution, system shutdown and system removal. If for any reason after design time architectural changes are needed after system launch (e.g., because requirements changed, or the implementation platform has evolved, etc) then the design process must be reexecuted from scratch (unless the changes are limited to parameter adjustment in the components deployed).

Dynamic designs allow for on the fly redesign of a component based system. A process for dynamic adaptation is able to reapply the design phases while the system is up and running, without stopping it (this is different from stop/redeploy/start). This kind of process supports chosen adaptation, when changes are planned and realized to maintain a good fit between the needs that the system must support and the way it supports them  [111] . Dynamic component-based designs rely on a component meta-model that supports complex life cycles for components, connectors, service specification, etc. Advanced dynamic designs can also take platform changes into account at run-time, without human intervention, by adapting themselves  [95] , [139] . Platform changes and more generally environmental changes trigger imposed adaptation, when the system can no longer use its design to provide the services it must support. In order to support an eternal system  [85] , dynamic component based systems must separate architectural design and platform compatibility. This requires support for heterogeneity, since platform evolutions can be partial.

The Models@runtime paradigm denotes a model-driven approach aiming at taming the complexity of dynamic software systems. It basically pushes the idea of reflection one step further by considering the reflection layer as a real model “something simpler, safer or cheaper than reality to avoid the complexity, danger and irreversibility of reality [130] ”. In practice, component-based (and/or service-based) platforms offer reflection APIs that make it possible to introspect the system (which components and bindings are currently in place in the system) and dynamic adaptation (by applying CRUD operations on these components and bindings). While some of these platforms offer rollback mechanisms to recover after an erroneous adaptation, the idea of Models@runtime is to prevent the system from actually enacting an erroneous adaptation. In other words, the “model at run-time” is a reflection model that can be uncoupled (for reasoning, validation, simulation purposes) and automatically resynchronized.

Heterogeneity is a key challenge for modern component based system. Until recently, component based techniques were designed to address a specific domain, such as embedded software for command and control, or distributed Web based service oriented architectures. The emergence of the Internet of Things paradigm calls for a unified approach in component based design techniques. By implementing an efficient separation of concern between platform independent architecture management and platform dependent implementations, Models@runtime is now established as a key technique to support dynamic component based designs. It provides DIVERSE with an essential foundation to explore an adaptation envelop at run-time.

Search Based Software Engineering [106] has been applied to various software engineering problems in order to support software developers in their daily work. The goal is to automatically explore a set of alternatives and assess their relevance with respect to the considered problem. These techniques have been applied to craft software architecture exhibiting high quality of services properties [103] . Multi Objectives Search based techniques [100] deal with optimization problem containing several (possibly conflicting) dimensions to optimize. These techniques provide DIVERSE with the scientific foundations for reasoning and efficiently exploring an envelope of software configurations at run-time.

Validation and verification

Validation and verification (V&V) theories and techniques provide the means to assess the validity of a software system with respect to a specific correctness envelop. As such, they form an essential element of DIVERSE's scientific background. In particular, we focus on model-based V&V in order to leverage the different models that specify the envelop at different moments of the software development lifecycle.

Model-based testing consists in analyzing a formal model of a system (e.g., activity diagrams, which capture high-level requirements about the system, statecharts, which capture the expected behavior of a software module, or a feature model, which describes all possible variants of the system) in order to generate test cases that will be executed against the system. Model-based testing [138] mainly relies on model analysis, constraint solving [99] and search-based reasoning [113] . DIVERSE leverages in particular the applications of model-based testing in the context of highly-configurable systems and [140] interactive systems [115] as well as recent advances based on diversity for test cases selection [107] .

Nowadays, it is possible to simulate various kinds of models. Existing tools range from industrial tools such as Simulink, Rhapsody or Telelogic to academic approaches like Omega [123] , or Xholon (http://www.primordion.com/Xholon/). All these simulation environments operate on homogeneous environment models. However, to handle diversity in software systems, we also leverage recent advances in heterogeneous simulation. Ptolemy [90] proposes a common abstract syntax, which represents the description of the model structure. These elements can be decorated using different directors that reflect the application of a specific model of computation on the model element. Metropolis [80] provides modeling elements amenable to semantically equivalent mathematical models. Metropolis offers a precise semantics flexible enough to support different models of computation. ModHel'X [105] studies the composition of multi-paradigm models relying on different models of computation.

Model-based testing and simulation are complemented by runtime fault-tolerance through the automatic generation of software variants that can run in parallel, to tackle the open nature of software-intensive systems. The foundations in this case are the seminal work about N-version programming [78] , recovery blocks [128] and code randomization [82] , which demonstrated the central role of diversity in software to ensure runtime resilience of complex systems. Such techniques rely on truly diverse software solutions in order to provide systems with the ability to react to events, which could not be predicted at design time and checked through testing or simulation.

Empirical software engineering

The rigorous, scientific evaluation of DIVERSE's contributions is an essential aspect of our research methodology. In addition to theoretical validation through formal analysis or complexity estimation, we also aim at applying state-of-the-art methodologies and principles of empirical software engineering. This approach encompasses a set of techniques for the sound validation contributions in the field of software engineering, ranging from statistically sound comparisons of techniques and large-scale data analysis to interviews and systematic literature reviews [133] , [131] . Such methods have been used for example to understand the impact of new software development paradigms [89] . Experimental design and statistical tests represent another major aspect of empirical software engineering. Addressing large-scale software engineering problems often requires the application of heuristics, and it is important to understand their effects through sound statistical analyses [77] .