Diversifying Focused Testing for Unit Testing

Software changes constantly, because developers add new features or modifications. This directly affects the effectiveness of test suite associated with that software, especially when these modifications are in a specific area no case covers. article tackles problem generating high-quality to cover repeatedly given point program, ultimate goal exposing faults possibly affecting program point. Both search-based software testing and constraint solving offer ready, but low-quality, solutions this: Ideally, maximally diverse covering set is required, whereas search tend generate sets biased distributions. Our approach, Diversified Focused Testing (DFT), uses strategy inspired by GödelTest. We artificially inject parameters into code branching conditions use bi-objective algorithm find inputs perturbing injected parameters, while keeping path still satisfiable. results demonstrate our technique, DFT, able desired at least 90% time. Moreover, adding diversity improves bug detection mutation killing abilities suites. show DFT achieves better than focused testing, symbolic execution, random achieving from 3% 70% improvement score up 100% fault across 105 subjects.