Symbolic search-based testing

Abstract

We present an algorithm for constructing fitness functions that improve the efficiency of search-based testing when trying to generate branch adequate test data. The algorithm combines symbolic information with dynamic analysis and has two key advantages: It does not require any change in the underlying test data generation technique and it avoids many problems traditionally associated with symbolic execution, in particular the presence of loops. We have evaluated the algorithm on industrial closed source and open source systems using both local and global search-based testing techniques, demonstrating that both are statistically significantly more efficient using our approach. The test for significance was done using a one-sided, paired Wilcoxon signed rank test. On average, the local search requires 23.41% and the global search 7.78% fewer fitness evaluations when using a symbolic execution based fitness function generated by the algorithm.