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Article
Automatic Fine-Grained Issue Report Reclassification
2014 19th International Conference on Engineering of Complex Computer Systems (ICECCS): August 4-7, Tianjin, Proceedings
  • Pavneet Singh Kochhar, Singapore Management University
  • Ferdian Thung, Singapore Management University
  • David LO, Singapore Management University
Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
8-2014
Abstract

Issue tracking systems are valuable resources during software maintenance activities. These systems contain different categories of issue reports such as bug, request for improvement (RFE), documentation, refactoring, task etc. While logging issue reports into a tracking system, reporters can indicate the category of the reports. Herzig et al. Recently reported that more than 40% of issue reports are given wrong categories in issue tracking systems. Among issue reports that are marked as bugs, more than 30% of them are not bug reports. The misclassification of issue reports can adversely affects developers as they then need to manually identify the categories of various issue reports. To address this problem, in this paper we propose an automated technique that reclassifies an issue report into an appropriate category. Our approach extracts various feature values from a bug report and predicts if a bug report needs to be reclassified and its reclassified category. We have evaluated our approach to reclassify more than 7,000 bug reports from HTTP Client, Jackrabbit, Lucene-Java, Rhino, and Tomcat 5 into 1 out of 13 categories. Our experiments show that we can achieve a weighted precision, recall, and F1 (F-measure) score in the ranges of 0.58-0.71, 0.61-0.72, and 0.57-0.71 respectively. In terms of F1, which is the harmonic mean of precision and recall, our approach can substantially outperform several baselines by 28.88%-416.66%.

Keywords
  • Fine-Grained,
  • Issue Reports,
  • Reclassification
ISBN
9781479954810
Identifier
10.1109/ICECCS.2014.25
Publisher
IEEE Computer Society
City or Country
Los Alamitos, CA
Copyright Owner and License
Publisher
Creative Commons License
Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International
Additional URL
https://doi.org/10.1109/ICECCS.2014.25
Citation Information
Pavneet Singh Kochhar, Ferdian Thung and David LO. "Automatic Fine-Grained Issue Report Reclassification" 2014 19th International Conference on Engineering of Complex Computer Systems (ICECCS): August 4-7, Tianjin, Proceedings (2014) p. 126 - 135
Available at: http://works.bepress.com/david_lo/195/