MUX: algorithm selection for software model checkers

Abstract

With the growing complexity of modern day software, software model checking has become a critical technology for ensuring correctness of software. As is true with any promising technology, there are a number of tools for software model checking. However, their respective performance trade-offs are difficult to characterize accurately – making it difficult for practitioners to select a suitable tool for the task at hand. This paper proposes a technique called MUX that addresses the problem of selecting the most suitable software model checker for a given input instance. MUX performs machine learning on a repository of software verification instances. The algorithm selector, synthesized through machine learning, uses structural features from an input instance, comprising a program-property pair, at runtime and determines which tool to use. We have implemented MUX for Windows device drivers and evaluated it on a number of drivers and model checkers. Our results are promising in that the algorithm selector not only avoids a significant number of timeouts but also improves the total runtime by a large margin, compared to any individual model checker. It also outperforms a portfolio-based algorithm selector being used in Microsoft at present. Besides, MUX identifies structural features of programs that are key factors in determining performance of model checkers.