ترغب بنشر مسار تعليمي؟ اضغط هنا

Proofs as Relational Invariants of Synthesized Execution Grammars

73   0   0.0 ( 0 )
 نشر من قبل Caleb Voss
 تاريخ النشر 2017
  مجال البحث الهندسة المعلوماتية
والبحث باللغة English




اسأل ChatGPT حول البحث

The automatic verification of programs that maintain unbounded low-level data structures is a critical and open problem. Analyzers and verifiers developed in previous work can synthesize invariants that only describe data structures of heavily restricted forms, or require an analyst to provide predicates over program data and structure that are used in a synthesized proof of correctness. In this work, we introduce a novel automatic safety verifier of programs that maintain low-level data structures, named LTTP. LTTP synthesizes proofs of program safety represented as a grammar of a given programs control paths, annotated with invariants that relate program state at distinct points within its path of execution. LTTP synthesizes such proofs completely automatically, using a novel inductive-synthesis algorithm. We have implemented LTTP as a verifier for JVM bytecode and applied it to verify the safety of a collection of verification benchmarks. Our results demonstrate that LTTP can be applied to automatically verify the safety of programs that are beyond the scope of previously-developed verifiers.



قيم البحث

اقرأ أيضاً

Symbolic execution is a powerful technique for program analysis. However, it has many limitations in practical applicability: the path explosion problem encumbers scalability, the need for language-specific implementation, the inability to handle com plex dependencies, and the limited expressiveness of theories supported by underlying satisfiability checkers. Often, relationships between variables of interest are not expressible directly as purely symbolic constraints. To this end, we present a new approach -- neuro-symbolic execution -- which learns an approximation of the relationship as a neural net. It features a constraint solver that can solve mixed constraints, involving both symbolic expressions and neural network representation. To do so, we envision such constraint solving as procedure combining SMT solving and gradient-based optimization. We demonstrate the utility of neuro-symbolic execution in constructing exploits for buffer overflows. We report success on 13/14 programs which have difficult constraints, known to require specialized extensions to symbolic execution. In addition, our technique solves $100$% of the given neuro-symbolic constraints in $73$ programs from standard verification and invariant synthesis benchmarks.
Verifying partial (i.e., termination-insensitive) equivalence of programs has significant practical applications in software development and education. Conventional equivalence verifiers typically rely on a combination of given relational summaries a nd suggested synchronization points; such information can be extremely difficult for programmers without a background in formal methods to provide for pairs of programs with dissimilar logic. In this work, we propose a completely automated verifier for determining partial equivalence, named Pequod. Pequod automatically synthesizes expressive proofs of equivalence conventionally only achievable via careful, manual constructions of product programs To do so, Pequod syntheses relational proofs for selected pairs of program paths and combines the per-path relational proofs to synthesize relational program invariants. To evaluate Pequod, we implemented it as a tool that targets Java Virtual Machine bytecode and applied it to verify the equivalence of hundreds of pairs of solutions submitted by students for problems hosted on popular online coding platforms, most of which could not be verified by existing techniques.
Lenses are a popular approach to bidirectional transformations, a generalisation of the view update problem in databases, in which we wish to make changes to source tables to effect a desired change on a view. However, perhaps surprisingly, lenses ha ve seldom actually been used to implement updatable views in databases. Bohannon, Pierce and Vaughan proposed an approach to updatable views called relational lenses, but to the best of our knowledge this proposal has not been implemented or evaluated to date. We propose incremental relational lenses, that equip relational lenses with change-propagating semantics that map small changes to the view to (potentially) small changes to the source tables. We also present a language-integrated implementation of relational lenses and a detailed experimental evaluation, showing orders of magnitude improvement over the non-incremental approach. Our work shows that relational lenses can be used to support expressive and efficient view updates at the language level, without relying on updatable view support from the underlying database.
In this paper, we present a derivative-based, functional recognizer and parser generator for visibly pushdown grammars. The generated parser accepts ambiguous grammars and produces a parse forest containing all valid parse trees for an input string i n linear time. Each parse tree in the forest can then be extracted also in linear time. Besides the parser generator, to allow more flexible forms of the visibly pushdown grammars, we also present a translator that converts a tagged CFG to a visibly pushdown grammar in a sound way, and the parse trees of the tagged CFG are further produced by running the semantic actions embedded in the parse trees of the translated visibly pushdown grammar. The performance of the parser is compared with a popular parsing tool ANTLR and other popular hand-crafted parsers. The correctness of the core parsing algorithm is formally verified in the proof assistant Coq.
93 - Sanjiva Prasad 2016
Based on the two observations that diverse applications perform better on different multicore architectures, and that different phases of an application may have vastly different resource requirements, Pal et al. proposed a novel reconfigurable hardw are approach for executing multithreaded programs. Instead of mapping a concurrent program to a fixed architecture, the architecture adaptively reconfigures itself to meet the applications concurrency and communication requirements, yielding significant improvements in performance. Based on our earlier abstract operational framework for multicore execution with hierarchical memory structures, we describe execution of multithreaded programs on reconfigurable architectures that support a variety of clustered configurations. Such reconfiguration may not preserve the semantics of programs due to the possible introduction of race conditions arising from concurrent accesses to shared memory by threads running on the different cores. We present an intuitive partial ordering notion on the cluster configurations, and show that the semantics of multithreaded programs is always preserved for reconfigurations upward in that ordering, whereas semantics preservation for arbitrary reconfigurations can be guaranteed for well-synchronised programs. We further show that a simple approximate notion of efficiency of execution on the different configurations can be obtained using the notion of amortised bisimulations, and extend it to dynamic reconfiguration.
التعليقات
جاري جلب التعليقات جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
mircosoft-partner

هل ترغب بارسال اشعارات عن اخر التحديثات في شمرا-اكاديميا