Multi-objective search-based approach to estimate issue resolution time

Abbood Al-Zubaidi, W, Dam, H, Ghose, A and Li, X 2017, 'Multi-objective search-based approach to estimate issue resolution time', in Proceedings of the 13th International Conference on Predictive Models and Data Analytics in Software Engineering, Toronto, Canada, 8 November 2017, pp. 53-62.


Document type: Conference Paper
Collection: Conference Papers

Title Multi-objective search-based approach to estimate issue resolution time
Author(s) Abbood Al-Zubaidi, W
Dam, H
Ghose, A
Li, X
Year 2017
Conference name 13th International Conference on Predictive Models and Data Analytics in Software Engineering
Conference location Toronto, Canada
Conference dates 8 November 2017
Proceedings title Proceedings of the 13th International Conference on Predictive Models and Data Analytics in Software Engineering
Publisher ACM
Place of publication New York, United States
Start page 53
End page 62
Total pages 10
Abstract Background: Resolving issues is central to modern agile software development where a software is developed and evolved incrementally through series of issue resolutions. An issue could represent a requirement for a new functionality, a report of a software bug or a description of a project task. Aims: Knowing how long an issue will be resolved is thus important to di?erent stakeholders including end-users, bug reporters, bug triagers, developers and managers. This paper aims to propose a multi-objective search-based approach to estimate the time required for resolving an issue. Methods: Using genetic programming (a meta-heuristic optimization method), we iteratively generate candidate estimate models and search for the optimal model in estimating issue resolution time. The search is guided simultaneously by two objectives: maximizing the accuracy of the estimation model while minimizing its complexity. Results: Our evaluation on 8,260 issues from five large open source projects demonstrate that our approach significantly (p < 0.001) outperforms both the baselines and state-of-the-art techniques. Conclusions: Evolutionary search-based approaches o?er an e?ective alternative to build estimation models for issue resolution time. Using multiple objectives, one for measuring the accuracy and the other for the complexity, helps produce accurate and simple estimation models.
Subjects Neural, Evolutionary and Fuzzy Computation
DOI - identifier 10.1145/3127005.3127011
Copyright notice © 2017 ACM
ISBN 9781450353052
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