Mining Temporal Information to Improve Duplication Detection on Bug Reports
Chao-Yuan Lee, Dandan Hu, Feng Zhong-yi, Cheng‐Zen Yang · 2015
Due to the vast amount of bug reports in many large-scale open source projects, detecting duplicate reports has received much attention in recent years. Textual information and different kinds of categorical information have been leveraged in many detection methods. Although past studies have shown the influence of the temporal information in the submission interval of duplicate bug reports, only few methods consider the temporal information in its plain interval form. In this paper, we propose a temporal model to improve the detection performance by considering the submission time and the version information. We discuss the effectiveness by implementing it based on a prestigious BM25Fext-based method. Empirical experiments have been conducted on a dataset collected from the Eclipse open source project. Compared with the baseline BM25Fext-based method, the proposed scheme can obtain performance improvement consistently.