Previous applications of symbolic execution (Sym-Exe) have focused on bug-finding and test-case generation. However, SymExe has the potential to significantly improve usability and automation when applied to verification of software contracts in safety-critical systems. Due to the lack of support for processing software contracts and ad hoc approaches for introducing a variety of over/under-approximations and optimizations, most SymExe implementations cannot precisely characterize the verification status of contracts. Moreover, these tools do not provide explicit justifications for their conclusions, and thus they are not aligned with trends toward evidence-based verification and certification. We introduce the concept of explicating symbolic execution (xSymExe) that builds on a strong semantic foundation, supports full verification of rich software contracts, explicitly tracks where over/under-approximations are introduced or avoided, precisely characterizes the verification status of each contractual claim, and associates each claim with explications for its reported verification status. We report on case studies in the use of Bakar Kiasan, our open source xSymExe tool for Spark Ada.

This tool paper describes the design and implementation of an interactive environment for discovering and browsing information flow in SPARK programs. SPARK is a subset of Ada that has been used in a number of industrial contexts for implementing certified safety and security critical systems. SPARK requires explicit specification of information flow properties in the form of procedure contracts. To write such contracts, developers need to understand the data and control dependencies in the program. Our tool Bakar Alir, implemented as an Eclipse Plug-in, utilizes classic slicing and chopping techniques to assist developers in writing information flow contracts.

Automated theorem proving techniques such as Satisfiability Modulo Theory (SMT) solvers have seen significant advances in the past several years. These advancements, coupled with vast hardware improvements, have drastic impact on, for example, program verification techniques and tools. The general availability of robust general purpose solvers have reduced a significant engineering overhead when designing and developing program verifiers. However, most solver implementations are designed to be used as a black box, and due to their aim as general purpose solvers, they often miss optimization opportunities that can be done by leveraging domain-specific knowledge.