Writing a Model Checker in 80 Days: Reusable Libraries and Custom Implementation

During a course on model checking we developed BMoth, a full-stack model checker for classical B, featuring both explicit-state and symbolic model checking. Given that we only had a single university term to finish the project, a particular focus was on reusing existing libraries to reduce implementation workload.In the following, we report on a selection of reusable libraries, which can be combined into a prototypical model checker relatively easily. Additionally, we discuss where custom code depending on the specification language to be checked is needed and where further optimization can take place. To conclude, we compare to other model checkers for classical B

Evaluating software verification systems: benchmarks and competitions

This report documents the program and the outcomes of Dagstuhl Seminar 14171 “Evaluating Software Verification Systems: Benchmarks and Competitions”. The seminar brought together a large group of current and future competition organizers and participants, benchmark maintainers, as well as practitioners and researchers interested in the topic. The seminar was conducted as a highly interactive event, with a wide spectrum of contributions from participants, including talks, tutorials, posters, tool demonstrations, hands-on sessions, and a live competition

Understanding The Impact of Solver Choice in Model-Based Test Generation

Background: In model-based test generation, SMT solvers explore the state-space of the model in search of violations of specified properties. If the solver finds that a predicate can be violated, it produces a partial test specification demonstrating the violation.Aims: The choice of solvers is important, as each may produce differing counterexamples. We aim to understand how solver choice impacts the effectiveness of generated test suites at finding faults.Method: We have performed experiments examining the impact of solver choice across multiple dimensions, examining the ability to attain goal satisfaction and fault detection when satisfaction is achieved---varying the source of test goals, data types of model input, and test oracle.Results: The results of our experiment show that solvers vary in their ability to produce counterexamples, and---for models where all solvers achieve goal satisfaction---in the resulting fault detection of the generated test suites. The choice of solver has an impact on the resulting test suite, regardless of the oracle, model structure, or source of testing goals.Conclusions: The results of this study identify factors that impact fault-detection effectiveness, and advice that could improve future approaches to model-based test generation

On abstraction refinement for program analyses in Datalog

A central task for a program analysis concerns how to efficiently find a program abstraction that keeps only information relevant for proving properties of interest. We present a new approach for finding such abstractions for program analyses written in Datalog. Our approach is based on counterexample-guided abstraction refinement: when a Datalog analysis run fails using an abstraction, it seeks to generalize the cause of the failure to other abstractions, and pick a new abstraction that avoids a similar failure. Our solution uses a boolean satisfiability formulation that is general, complete, and optimal: it is independent of the Datalog solver, it generalizes the failure of an abstraction to as many other abstractions as possible, and it identifies the cheapest refined abstraction to try next. We show the performance of our approach on a pointer analysis and a typestate analysis, on eight real-world Java benchmark programs