Software defect prediction using artificial immune recognition system

Abstract

Predicting fault-prone modules for software development projects enables companies to reach high reliable systems and minimizes necessary budget, personnel and resource to be allocated to achieve this goal. Researchers have investigated various statistical techniques and machine learning algorithms until now but most of them applied their models to the different datasets which are not public or used different criteria to decide the best predictor model. Artificial Immune Recognition System is a supervised learning algorithm which has been proposed in 2001 for the classification problems and its performance for UCI datasets (University of California machine learning repository) is remarkable. In this paper, we propose a novel software defect prediction model by applying Artificial Immune Recognition System (AIRS) along with the Correlation-Based Feature Selection (CFS) technique. In order to evaluate the performance of the proposed model, we apply it to the five NASA public defect datasets and compute G-mean1, G-mean2 and F-measure values to discuss the effectiveness of the model. Experimental results show that AIRS has a great potential for software defect prediction and AIRS along with CFS technique provides relatively better prediction for large scale projects which consist of many modules.