Performance Improvement of Automated Test Input Data Generation by Using Value Concretization

Automated generation of test input data is a major technical challenge in software engineering research. We are developing a tool for automated test input data generation for COBOL, a language widely used in enterprise applications. As in prior studies, we employ a hybrid approach that combines symbolic execution and constraint solving. In addition, to accommodate the various data representations in COBOL, we introduce a byte-level data representation into the memory model used by our tool. However, this increases constraint complexity and size, leading to degraded solver performance. To address this issue, we propose transforming constraint logic formulae through value concretization. Our evaluation shows that this technique effectively alleviates the performance problem.