A new method for software test data generation inspired by D-algorithm

Test generation for digital hardware is highly automated, scalable (in practice), and provides high test quality. In contrast, current software automatic test data generation approaches suffer from low test quality or high complexity. While mutation-oriented constraint-based test data generation for software was proposed to generate high quality test data for real program bugs, all existing approaches require symbolic analysis for the whole program, and hence are not scalable even for unit testing, i.e., testing the lowest-level software modules. We propose a new method inspired by hardware D-algorithm and divide and conquer for software test data generation. To reduce runtime complexity and improve scalability, we combine global structural analysis and a sequence of small reusable symbolic analyses of parts of the program, instead of symbolically executing each mutated version of the entire program. We also propose a multi-pass test generation system to further reduce runtime complexity and compact test data. We compare our tools with one of the best software test generation tools (EvoSuite[20], which won the SBST 2017 tool competition) and demonstrate that our approach generates higher quality unit tests in a scalable manner and provides a compact set of tests.