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Projects APR

Team : APR

  • MOPSA - Modular Open Platform for Static Analysis

    The Mopsa project aims at creating methods and tools to make computer software more reliable. Programming errors are pervasive with results ranging from user frustration to huge economical or human losses. Traditional test-based methods are insufficient to eliminate all errors. The project will develop static analyses able to detect at compile-time whole classes of program defects, leveraging the theory of abstract interpretation to design analyses that are approximate (to scale up to large programs) and sound (no defect is missed). Static analysis has enjoyed recent successes: Astrée, an industrial analyzer, was able to prove the absence of run-time error in Airbus software. But such results are limited to the specific, well-controlled context of critical embedded systems. We wish to bring static analysis to the next level: target larger, more complex and heterogeneous software, and make it usable by engineers to improve general-purpose software. We focus on analyzing open-source software which are readily available, complex, widespread, and important from an economical standpoint (they are used in many infrastructures and companies) but also societal and educational ones (promoting the development of verified software for and by citizens). A major target we consider is the set of technologies at the core of Internet on which static analysis could be applied to ensure a safer Internet. The scientific challenges we must overcome include designing scalable analyses producing relevant information, supporting novel popular languages (such as Python), analyzing properties more adapted to the continuous development of software common in open-source. At the core of the project is the construction of an open-source static analysis platform. It will serve not only to implement and evaluate the results of the project, but also create a momentum encouraging the research in static analysis and hasten its adoption in open-source development communities.

    Project leader : Antoine MINÉ
    More details here
  • COVERIF - Combining abstract interpretation and constraint programming to verify critical properties of embedded programs with floating point computations

    Verifying correctness and robustness of programs and systems is a major challenge in a society which relies more and more on safety-critical systems controlled by embedded software. This issue is even more critical when the computations involve floating-point number arithmetic, an arithmetic known for its quite unusual behaviors, and which is increasingly used in embedded software. Note for example the "catastrophic cancellation" phenomenon where most of the significant digits of a result are cancelled or, numerical sequences whose limit is very different over the real numbers and over the floating-point numbers. A more important problem arises when we want to analyse the relationship between floating-point computations and an "idealized" computation that would be carried out with real numbers, the reference in the design of the program. The point is that for some input values, the control flow over the real numbers can go through one conditional branch while it goes through another one over the floating-point numbers. Certifying that a program, despite some control flow divergences, computes what it is actually expected to compute with a minimum error is the subject of the robustness or continuity analysis. Providing a set of techniques and tools for verifying the accuracy, correctness and robustness for critical embedded software is a major challenge. The aim of this project is to address this challenge by exploring new methods based on a tight collaboration between abstract interpretation (IA) and constraint programming (CP). In other words, the goal is to push the limits of these two techniques for improving accuracy analysis, to enable a more complete verification of programs using floating point computations, and thus, to make critical decisions more robust. The cornerstone of this project is the combination of the two approaches to increase the accuracy of the proof of robustness by using PPC techniques, and, where appropriate, to generate non-robust test cases. The goal is to benefit from the strengths of both techniques: PPC provides powerful but computationally expensive algorithms to reduce domains with an arbitrary given precision whereas AI does not provide fine control over domain precision, but has developed many abstract domains that quickly capture program invariants of various forms. Incorporating some PPC mechanisms (search tree, heuristics) in abstract domains would enable, in the presence of false alarms, to refine the abstract domain by using a better accuracy. The first problem to solve is to set the theoretical foundations of an analyser based on two substantially different paradigms. Once the interactions between PPC and IA are well formalized, the next issue is to handle constraints of general forms and potentially non-linear abstract domains. Last but not least, an important issue concerns the robustness analysis of more general systems than programs, like hybrid systems which are modeling control command programs. Research results will be evaluated on realistic benchmarks coming from industrial companies, in order to determine their benefits and relevance. For the explored approaches, using realistic examples is a key point since the proposed techniques often only behave in an acceptable manner on a given sub classes of problems (if we consider the worst-case computational complexity all these problems are intractable). That's why many solutions are closely connected to the target problems.

    Project leader : Eric GOUBAULT
    More details here
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