Predicting the Objective and Priority of Issue Reports in a Cross project Context
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Proper documentation plays an important role in successful software management and maintenance. Software repositories such as GitHub host a large number of software entities. Developers collaboratively discuss, implement, use, and share these entities. 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 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. We train and evaluate our models in both project-based and cross-project settings. The latter approach provides a generic prediction model applicable to 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 83
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