Section: New Software and Platforms

Prototypes

Logical Causality

Participant : Gregor Goessler.

We are developing LoCa , a prototype tool written in Scala that implements the analysis of logical causality described in  6.3.3 . LoCa currently supports causality analysis in Bip and networks of timed automata. The core analysis engine is implemented as an abstract class, such that support for other models of computation (MoC) can be added by instantiating the class with the basic operations of the MoC.

Cosyma

Participant : Gregor Goessler.

We have developed Cosyma , a tool for automatic controller synthesis for incrementally stable switched systems based on multi-scale discrete abstractions. The tool accepts a description of a switched system represented by a set of differential equations and the sampling parameters used to define an approximation of the state-space on which discrete abstractions are computed. The tool generates a controller — if it exists — for the system that enforces a given safety or time-bounded reachability specification.

The SIAAM virtual machine

Participant : Jean-Bernard Stefani.

The SIAAM abstract machine is an object-based realization of the Actor model of concurrent computation. Actors can exchange arbitrary object graphs in messages while still enjoying a strong isolation property. It guarantees that each actor can only directly access objects in its own local heap, and that information between actors can only flow via message exchange. The SIAAM machine has been implemented for Java as a modified Jikes virtual machine. The resulting SIAAM software comprises:

• A modified Jikes RVM that implements actors and actor isolation as specified by the SIAAM machine.

• A set of static analyses build using the Soot Java optimization framework for optimizing the execution of the SIAAM/Jikes virtual machine, and for helping programmers diagnose potential performance issues.

• A formal proof using the Coq proof assistant of the SIAAM isolation property.

The SIAAM machine is the subject of Quentin Sabah's PhD thesis  [67] .

pyCPA_TCA

Participant : Sophie Quinton.

We are developing pyCPA_TCA , a pyCPA plugin for Typical Worst-Case Analysis as described in Section  6.2.2 . pyCPA is an open-source Python implementation of Compositional Performance Analysis developed at TU Braunschweig, which allows in particular response-time analysis. pyCPA_TCA is an extension of this tool that is co-developed by Sophie Quinton and Zain Hammadeh at TU Braunschweig. It allows in particular the computation of weakly-hard guarantees for real-time tasks, i.e. number of deadline misses out of a sequence of executions. So far, pyCPA_TCA is restricted to uniprocessor systems of independent tasks, scheduled according to static priority scheduling.