MaintainoMATE: A GitHub App for Intelligent Automation of Maintenance Activities

• URL: http://arxiv.org/abs/2308.16464v1
• Date: Thu, 31 Aug 2023 05:15:42 GMT
• Title: MaintainoMATE: A GitHub App for Intelligent Automation of Maintenance Activities
• Authors: Anas Nadeem, Muhammad Usman Sarwar, Muhammad Zubair Malik
• Abstract summary: Software development projects rely on issue tracking systems at the core of tracking maintenance tasks such as bug reports, and enhancement requests. The handling of issue-reports is critical and requires thorough scanning of the text entered in an issue-report making it a labor-intensive task. We present a unified framework called MaintainoMATE, which is capable of automatically categorizing the issue-reports in their respective category and further assigning the issue-reports to a developer with relevant expertise.
• Score: 3.2228025627337864
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Software development projects rely on issue tracking systems at the core of tracking maintenance tasks such as bug reports, and enhancement requests. Incoming issue-reports on these issue tracking systems must be managed in an effective manner. First, they must be labelled and then assigned to a particular developer with relevant expertise. This handling of issue-reports is critical and requires thorough scanning of the text entered in an issue-report making it a labor-intensive task. In this paper, we present a unified framework called MaintainoMATE, which is capable of automatically categorizing the issue-reports in their respective category and further assigning the issue-reports to a developer with relevant expertise. We use the Bidirectional Encoder Representations from Transformers (BERT), as an underlying model for MaintainoMATE to learn the contextual information for automatic issue-report labeling and assignment tasks. We deploy the framework used in this work as a GitHub application. We empirically evaluate our approach on GitHub issue-reports to show its capability of assigning labels to the issue-reports. We were able to achieve an F1-score close to 80\%, which is comparable to existing state-of-the-art results. Similarly, our initial evaluations show that we can assign relevant developers to the issue-reports with an F1 score of 54\%, which is a significant improvement over existing approaches. Our initial findings suggest that MaintainoMATE has the potential of improving software quality and reducing maintenance costs by accurately automating activities involved in the maintenance processes. Our future work would be directed towards improving the issue-assignment module.

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