Predicting Number of Faults in Software System using Genetic Programming

In software development perspective, dealing with software faults is a vital and foremost important task. Presence of faults not only reduces the quality of the software, but also increases its development cost. A large number of models have been presented in the past to predict the fault proneness of the software system. However, most of them provide inadequate information and thus make the task of fault prediction difficult. In this paper, we present an approach to predict the number of faults in the given software system using the Genetic Programming (GP). We validate the proposed approach using an experimental investigation where we use the fault datasets of the ten software projects available in the PROMISE data repository. The Error rate, Recall and Completeness of the fault prediction model are used to evaluate the performance of the proposed approach. The results show that GP based models have produced the significant results for the number of faults prediction.