Mining Bug Classifier and Debug Strategy Association Rules for Web-Based Applications

Authors:
Lian Yu;Changzhu Kong;Lei Xu;Jingtao Zhao;Huihui Zhang
Affiliations:
The School of Software and Microelectronics, Peking University, Beijing, P.R. China 102600;The School of Software and Microelectronics, Peking University, Beijing, P.R. China 102600;The School of Software and Microelectronics, Peking University, Beijing, P.R. China 102600;The School of Software and Microelectronics, Peking University, Beijing, P.R. China 102600;The School of Software and Microelectronics, Peking University, Beijing, P.R. China 102600
Venue:
ADMA '08 Proceedings of the 4th international conference on Advanced Data Mining and Applications
Year:
2008

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Abstract

The paper uses data mining approaches to classify bug types and excavate debug strategy association rules for Web-based applications. Chi-square algorithm is used to extract bug features, and SVM to model bug classifier achieving more than 70% predication accuracy on average. Debug strategy association rules accumulate bug fixing knowledge and experiences regarding to typical bug types, and can be applied repeatedly, thus improving the bug fixing efficiency. With 575 training data, three debug strategy association rules are unearthed.