Application of Evolutionary Algorithms for Regression Suites Optimization

Regression test suites are necessary to ensure that changes to the system made after bug fixes or reimplementation have not corrupted the intended functionality. However, because of the complexity of current hardware systems, it is desirable to have optimized regression suites that provide the highest verification coverage with minimal simulation time and resources. In this paper, we introduce a coverage-directed optimization algorithm for creating optimized regression suites from verification stimuli that were evaluated in simulation-based verification environment. The results of our experiments show that the size of the final regression suites are significantly improved in comparison to the original test suit. For our experimental system, we were able to eliminate 94.4% redundant stimuli from the original test suite while retaining the same level of coverage.