B aton : symphony of random testing and concolic testing through machine learning and taint analysis

Random testing is scalable but often fails to hit corner program behaviors, while systematic testing (e.g., concolic execution) is promising to cover corner program behaviors but is not scalable to explore all program behaviors. Prior attempts to integrate random testing with systematic testing lack targeted guidance. In this paper, we propose a guided hybrid testing approach, named B aton , to synergize random testing with concolic testing. It integrates the knowledge inside test cases and their executions into a conditional execution graph, and uses such knowledge to guide test case generation. Specifically, we learn classification models for some conditionals in the conditional execution graph in a demand-driven way. These models are used to guide random testing to reach and cover partially-covered conditionals. We further employ targeted concolic testing to cover conditionals that cannot be fully covered by guided random testing. We implemented B aton for Java and evaluated it on three benchmarks. The results show that B aton improved branch coverage and mutation score over random testing by 16.2%–29.4% and 19.0%–30.0%, over adaptive random testing by 16.8%–33.8% and 19.4%–34.2%, over concolic testing by 2.3%–29.9% and 2.9%–30.1%, and over simple hybrid testing by 1.6%–14.5% and 1.4%–18.7%.