A Feature Selection Framework for Software Defect Prediction Using ISFLA

Abstract

Aiming at the problem of feature space dimension reduction and large search space in feature selection of software defect, a defect prediction feature selection framework based on meta-heuristic search algorithm (ISFLA) is proposed. The framework improves generalization of predictions of unknown data samples, enhances the ability to search for features related to learning tasks, and completes further reductions in feature space dimensions. Using some NASA data sets, several common software defect prediction methods and ISFLA simulation experiments were carried out. The experimental results show that the software feature selection framework based on the improved shuffled frog leaping algorithm effectively improves the performance of software defect prediction.