Test suite optimization using fuzzy logic

Regression Test suite optimization is an effective technique to reduce time and cost of testing. Many researchers have used computational intelligence techniques to enhance the effectiveness of test suite. These approaches optimize test suite for a single objective. Introduction of fuzzy logic with genetic algorithm and swarm optimization may be used to optimize test suite for multi-objective selection criteria. Secondly, human intervention or expert judgment is required to opt for level of testing, technique used and quality aspect to be tested. Fuzzy logic has proved its worth in many other domains like communication, bio informatics, embedded applications, industrial and engineering control and network optimization. We propose an expert system that finds a trade off among the quality aspects, technique used and level of testing based on objective function defined by the tester, quite similar to human judgment using fuzzy logic based classification. Main focus of our approach is to find a test suite that is optimal for multi-objective regression testing. For proof of our concept, initially we focused on three quality aspects, i.e., performance, throughput and code coverage.