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Erschienen in: Empirical Software Engineering 2/2022

01.03.2022

Predicting the objective and priority of issue reports in software repositories

verfasst von: Maliheh Izadi, Kiana Akbari, Abbas Heydarnoori

Erschienen in: Empirical Software Engineering | Ausgabe 2/2022

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Abstract

Software repositories such as GitHub host a large number of software entities. Developers collaboratively discuss, implement, use, and share these entities. Proper documentation plays an important role in successful software management and maintenance. Users exploit Issue Tracking Systems, a facility of software repositories, to keep track of issue reports, to manage the workload and processes, and finally, to document the highlight of their team’s effort. An issue report is a rich source of collaboratively-curated software knowledge, and can contain a reported problem, a request for new features, or merely a question about the software product. As the number of these issues increases, it becomes harder to manage them manually. GitHub provides labels for tagging issues, as a means of issue management. However, about half of the issues in GitHub’s top 1000 repositories do not have any labels. In this work, we aim at automating the process of managing issue reports for software teams. We propose a two-stage approach to predict both the objective behind opening an issue and its priority level using feature engineering methods and state-of-the-art text classifiers. To the best of our knowledge, we are the first to fine-tune a Transformer for issue classification. We train and evaluate our models in both project-based and cross-project settings. The latter approach provides a generic prediction model applicable for any unseen software project or projects with little historical data. Our proposed approach can successfully predict the objective and priority level of issue reports with \(82\%\) (fine-tuned RoBERTa) and \(75\%\) (Random Forest) accuracy, respectively. Moreover, we conducted human labeling and evaluation on unlabeled issues from six unseen GitHub projects to assess the performance of the cross-project model on new data. The model achieves \(90\%\) accuracy on the sample set. We measure inter-rater reliability and obtain an average Percent Agreement of \(85.3\%\) and Randolph’s free-marginal Kappa of 0.71 that translate to a substantial agreement among labelers.

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Fußnoten
1
https://github.com/MalihehIzadi/IssueReportsManagement
 
2
https://zenodo.org/record/4925855#.YNME2r4zbtQ
 
3
https://developer.github.com/v3/
 
4
https://api.github.com/search/repositories?q=stars:>500&sort=stars
 
5
https://docs.github.com/en/issues/using-labels-and-milestones-to-track-work/managing-labels
 
6
https://github.com/casics/spiral.
 
7
https://www.nltk.org/
 
8
http://sentistrength.wlv.ac.uk/
 
9
https://textblob.readthedocs.io/en/dev/
 
10
A complete list of these 66 clusters is available in our repository.
 
11
https://github.com/MalihehIzadi/IssueReportsManagement
 
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Metadaten
Titel
Predicting the objective and priority of issue reports in software repositories
verfasst von
Maliheh Izadi
Kiana Akbari
Abbas Heydarnoori
Publikationsdatum
01.03.2022
Verlag
Springer US
Erschienen in
Empirical Software Engineering / Ausgabe 2/2022
Print ISSN: 1382-3256
Elektronische ISSN: 1573-7616
DOI
https://doi.org/10.1007/s10664-021-10085-3

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