Using Test Ranges to Improve Symbolic Execution

Symbolic execution is a powerful systematic technique for checking programs, which has received a lot of research attention during the last decade. In practice however, the technique remains hard to scale. This paper introduces SynergiSE, a novel approach to improve symbolic execution by tackling a key bottleneck to its wider adoption: costly and incomplete constraint solving. To mitigate the cost, SynergiSE introduces a succinct encoding of constraint solving results, thereby enabling symbolic execution to be distributed among different workers while sharing and re-using constraint solving results among them without having to communicate databases of constraint solving results. To mitigate the incompleteness, SynergiSE introduces an integration of complementary approaches for testing, e.g., search-based test generation, with symbolic execution, thereby enabling symbolic execution and other techniques to apply in tandem. Experimental results using a suite of Java programs show that SynergiSE presents a promising approach for improving symbolic execution.