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Mining bug classifier and debug strategy association rules for web-based applications

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (2008) 5139 LNAI 427-434
DOI: 10.1007/978-3-540-88192-6_40
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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. © 2008 Springer-Verlag Berlin Heidelberg.

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Yu, L., Kong, C., Xu, L., Zhao, J., & Zhang, H. (2008). Mining bug classifier and debug strategy association rules for web-based applications. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5139 LNAI, pp. 427–434). Springer Verlag. https://doi.org/10.1007/978-3-540-88192-6_40

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