Breeding software test cases with genetic algorithms

Faulty software is usually costly and possibly life threatening as software becomes an increasingly critical component in a wide variety of systems. Thorough software testing by both developers and dedicated quality assurance staff is one way to uncover flaws. Automated test generation techniques can be used to augment the process, free of the cognitive biases that have been found in human testers. This paper focuses on breeding software test cases using genetic algorithms as part of a software testing cycle. An evolving fitness function that relies on a fossil record of organisms results in interesting search behaviours, based on the concepts of novelty, proximity, and severity. A case study that uses a simple, but widely studied program is used to illustrate the approach. Several visualization techniques are also introduced to analyze particular fossil records, as well as the overall search process.