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Classifying software issue reports through association mining

Zolkeply, Mohd Syafiq and Shao, Jianhua 2019. Classifying software issue reports through association mining. Presented at: 34th ACM/SIGAPP Symposium on Applied Computing, Limassol, Cyprus, 8-12 Apr 2019. SAC '19 Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing. ACM, pp. 1860-1863. 10.1145/3297280.3297608

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Abstract

Software issue reports classification is a significant task in software maintenance and evolution. Despite the research effort being made over the years, the existing issue reports classification techniques are still inadequate. In this paper, we propose a new approach that is inspired by the Classification Associations Rule Mining (CARM) methodology in data mining, and report the testing of our method on 500 software issue reports extracted from an open source issue tracking system. Our experiments show that our method can achieve a high degree of accuracy in classifying software issue reports.

Item Type: Conference or Workshop Item (Paper)
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Publisher: ACM
ISBN: 9781450359337
Date of First Compliant Deposit: 9 August 2019
Date of Acceptance: 28 November 2018
Last Modified: 30 Nov 2019 06:33
URI: http://orca.cf.ac.uk/id/eprint/124831

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