A Multi-Objective Learning Method for Building Sparse Defect Prediction Models

Abstract

Software defect prediction constructs a model from the previous version of a software project to predict defects in the current version, which can help software testers to focus on software modules with more defects in the current version. Most existing methods construct defect prediction models through minimizing the defect prediction error measures. Some researchers proposed model construction approaches that directly optimized the ranking performance in order to achieve an accurate order. In some situations, the model complexity is also considered. Therefore, defect prediction can be seen as a multi-objective optimization problem and should be solved by multi-objective approaches. And hence, in this paper, we employ an existing multi-objective evolutionary algorithm and propose a new multi-objective learning method based on it, to construct defect prediction models by simultaneously optimizing more than one goal. Experimental results over 30 sets of cross-version data show the effectiveness of the proposed multi-objective approaches.