Information retrieval on bug locations by learning co-located bug report clusters

Authors:
Ing-Xiang Chen;Hojun Jaygarl;Cheng-Zen Yang;Ping-Jung Wu
Affiliations:
Yuan Ze University, Chungli, Taiwan Roc;Iowa State University, Ames, IA, USA;Yuan Ze University, Chungli, Taiwan Roc;Yuan Ze University, Chungli, Taiwan Roc
Venue:
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Year:
2008

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Abstract

Bug locating usually involves intensive search activities and incurs unpredictable cost of labor and time. An issue of information retrieval on bug locations is particularly addressed to facilitate identifying bugs from software code. In this paper, a novel bug retrieval approach with co-location shrinkage (CS) is proposed. The proposed approach has been implemented in open-source software projects collected from real-world repositories, and consistently improves the retrieval accuracy of a state-of-the-art Support Vector Machine (SVM) model.