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Genetic Feature Selection for Software Defect Prediction

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Recently, software defect prediction is an important research topic in the software engineering field. The accurate prediction of defect prone software modules can help the software testing effort, reduce costs, and improve the software testing process by focusing on fault-prone module. Software defect data sets have an imbalanced nature with very few defective modules compared to defect-free ones. The software defect prediction performance also decreases significantly because the dataset contains noisy attributes. In this research, we propose the combination of genetic algorithm and bagging technique for improving the performance of the software defect prediction. Genetic algorithm is applied to deal with the feature selection, and bagging technique is employed to deal with the class imbalance problem. The proposed method is evaluated using the data sets from NASA metric data repository. Results have indicated that the proposed method makes an impressive improvement in prediction performance for most classifiers.

Document Type: Research Article

Publication date: January 1, 2014

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  • ADVANCED SCIENCE LETTERS is an international peer-reviewed journal with a very wide-ranging coverage, consolidates research activities in all areas of (1) Physical Sciences, (2) Biological Sciences, (3) Mathematical Sciences, (4) Engineering, (5) Computer and Information Sciences, and (6) Geosciences to publish original short communications, full research papers and timely brief (mini) reviews with authors photo and biography encompassing the basic and applied research and current developments in educational aspects of these scientific areas.
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