An intelligent model for software project risk prediction. Hu, Y., Zhang, X., Sun, X., Liu, M., and Du, J.Elsevier Science Inc., New York, NY, USA. Elements of Software Science (Operating and programming systems series). The WEKA data mining software: An update. Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., and Witten, I.Journal of Systems and Software, 81(2):186-195. Applying machine learning to software fault-proneness prediction. Predicting defectprone software modules using support vector machines. Journal of Machine Learning Research, pages 1-30. Statistical comparisons of classifiers over multiple data sets. In Proceedings of the 2005 workshop on Predictor models in software engineering, PROMISE 7805. Nearest neighbor sampling for better defect prediction. Promise repository of empirical software engineering data repository, west virginia university, department of computer science. Boetticher, G., Menzies, T., and Ostrand, T.The 18th IEEE International Symposium on. Data mining techniques for building fault-proneness models in telecom java software. Based on the results of our analysis, the developers can focus on more defective modules rather than on less or non defective ones during testing activities. The results of this study suggest that source code similarity is a good means of predicting the number of defects in software modules. The method proposed is also comparable with other regression methods like linear regression and IBK.
The experiments on 10 Promise datasets indicate that SVM with a precomputed kernel performs as good as the SVM with the usual linear or RBF kernels in terms of the root mean square error (RMSE). Each value in the kernel matrix shows how much similarity exists between the files or classes of the software system tested. To model the relationship between source code similarity and defectiveness, we use SVM with a precomputed kernel matrix. In this paper, we propose a novel method based on SVM to predict the number of defects in the files or classes of a software system. SCITEPRESS - SCIENCE AND TECHNOLOGY PUBLICATIONS A Novel Regression Method for Software Defect Prediction with Kernel MethodsĪhmet Okutan, Olcay Taner Yıldız 2013 Abstract
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