Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
7-2020
Abstract
Addressing issue reports is an integral part of open source software (OSS) projects. Although several studies have attempted to discover the factors that affect issue resolution, few pay attention to the underlying micro-process patterns of resolution processes. Discovering these micro-patterns will help us understand the dynamics of issue resolution processes so that we can manage and improve them in better ways. Of the various types of issues, those relating to corrective maintenance account for nearly half hence resolving these issues efficiently is critical for the success of OSS projects. Therefore, we apply process mining techniques to discover the micro-patterns of resolution processes for issues relating to corrective maintenance. Four and five typical patterns are found for the identification stage and solving stage of the resolution processes respectively. Furthermore, it is shown that the consequent patterns can be predicted with a certain degree of accuracy by selecting the appropriate features and models. Furthermore, we make use of the pattern information predicted to forecast the issue lifetime and the results show that this information can also improve the accuracy in the earlier observation points. At the same time, pattern predictions provide good interpretability to the forecast of issue lifetime.
Keywords
Issue lifetime prediction, Issue pattern prediction, Issue resolution, Micro-pattern, Process mining
Discipline
Software Engineering
Research Areas
Software and Cyber-Physical Systems
Publication
Proceedings of the 32nd International Conference on Software Engineering and Knowledge Engineering (SEKE 2020): July 9-19, Virtual
First Page
477
Last Page
482
ISBN
1891706500
Identifier
10.18293/SEKE2020-031
Publisher
Knowledge Systems Institute Graduate School
City or Country
Pittsburgh
Citation
WANG, Yiran; CAO, Jian; and LO, David.
Mining and predicting micro-process patterns of issue resolution for open source software projects. (2020). Proceedings of the 32nd International Conference on Software Engineering and Knowledge Engineering (SEKE 2020): July 9-19, Virtual. 477-482.
Available at: https://ink.library.smu.edu.sg/sis_research/5628
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.18293/SEKE2020-031