Conditional Quantitative Program Analysis

Standards for certifying safety-critical systems have evolved to permit the inclusion of evidence generated by program analysis and verification techniques. The past decade has witnessed the development of several program analyses that are capable of computing guarantees on bounds for the probability of failure. This paper develops a novel program analysis framework, CQA, that combines evidence from different underlying analyses to compute bounds on failure probability. It reports on an evaluation of different CQA-enabled analyses and implementations of state-of-the-art quantitative analyses to evaluate their relative strengths and weaknesses. To conduct this evaluation, we filter an existing verification benchmark to reflect certification evidence generation challenges. Our evaluation across the resulting set of 136 C programs, totaling more than 385k SLOC, each with a probability of failure below
$10^{-4}$
, demonstrates how CQA extends the state-of-the-art. The CQA infrastructure, including tools, subjects, and generated data is publicly available at
bitbucket.org/mgerrard/cqa
.