A novel regression method for software defect prediction with kernel methods
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Tarih
2013
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info:eu-repo/semantics/closedAccess
Özet
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. To model the relationship between source code similarity and defectiveness, we use SVM with a precomputed kernel matrix. Each value in the kernel matrix shows how much similarity exists between the files or classes of the software system tested. 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). The method proposed is also comparable with other regression methods like linear regression and IBK. The results of this study suggest that source code similarity is a good means of predicting the number of defects in software modules. 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.
Açıklama
Anahtar Kelimeler
Computer software, Defect prediction, Defects, Forecasting, Kernel methods, Mean square error, Pattern recognition, Regression analysis, Regression method, Root mean square errors, Software defects, Software engineering, Software defect prediction, Software modules, Source code similarities, SVM
Kaynak
Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods
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Künye
Okutan, A. & Yıldız, O. T. (2013). A novel regression method for software defect prediction with kernel methods. Paper present at the Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods, 216-221.
