COST SENSITIVE SOFTWARE DEFECT PREDICTION TECHNIQUE USING GENETIC ALGORITHM

Research has yielded approaches to predict future defects in software artifacts based on historical information, thus assisting companies in effectively allocating limited development resources and developers in reviewing each other's' code reduces the cost of maintenance. Developers are unlikely to devote the same effort to inspect each software artifact predicted to contain defects, since the effort varies with the artifacts' size (the number of LOC and cost) of defects that it exhibits (effectiveness). We propose to use Genetic Algorithms (GAs) for training prediction models to maximize their cost-effectiveness. We evaluate the approach on two well-known models, Regression Tree and Generalized Linear Model, and predict defects between multiple releases of 5 open source projects. Our results show that regression models trained by GAs significantly outperform their conventional counterparts, improving the costeffectiveness by up to 120 %.