Software Defect Prediction Using Enhanced Swarm Intelligence

Abstract

Software defect prediction is significant contributor in better understanding and controlling software quality. Many organizations want to predict the number of defects in software systems before they are deployed, in order to gauge the likely delivered quality and maintenance effort. A defect prediction model’s main objective is to identify error-prone parts of a software system as early during software lifecycle as possible, since costs of finding and correcting software defects later in the lifecycle rise prohibitively. Accurate prediction of defect‐ prone software modules can reduce costs and improve software testing process by focusing on fault- prone modules. Ant Colony Optimization (ACO) algorithm is a meta-heuristic technique for finding optimal search paths in graphs. This algorithm is a member of swarm intelligence methods, and mimics the behavior of ants trying to find a path between their colony and a food source. It is a very good combinatorial optimization method, possessing attributes like parallelism, convergence to good solutions, , and ease of hybridization with other methods. In this research, ACO-MB a combination of ACO along with modified bagging technique is proposed for software defect prediction. The proposed approach first assesses improvement in training through bagging hit and trial approach and then only in case of improvement it trains the predictor using ACO and records misclassification rate. This process continues as long as improvement in the predictor performance is found.