An effective Seed Selection Method for Maximizing the Performance of Symbolic Execution

Jaehyeok Lee, Sooyoung Cha

http://doi.org/10.5626/JOK.2025.52.7.578

Symbolic execution is a promising software testing technique that aims to maximize the coverage of executed code areas of program under test by effectively generating test cases. A well-known challenge in symbolic execution is the high cost associated with solving path conditions. One solution to this challenge is to utilize generated test cases as seed inputs for a symbolic execution tool, thereby reducing the number of required solver calls. The effectiveness of this solution depends on the selection of appropriate test cases as seed inputs. This paper proposes a method to enhance the performance of symbolic execution by grouping the generated test cases into clusters, identifying the most promising cluster and selecting the most potential seed input from within it. Experimentally, the proposed method achieved 42.0% more branch coverage on average than traditional symbolic execution tools without seed inputs.