Bug or not bug? That is the question
This addresses the issue of misclassification in software development tools like Jira, which can impact automatic management and metrics, but it is incremental as it builds on existing classification methods.
The paper tackles the problem of automatically classifying software issues as bugs or non-bugs to assist backlog management, achieving significant improvements in F1 measure across datasets compared to existing works.
Nowadays, development teams often rely on tools such as Jira or Bugzilla to manage backlogs of issues to be solved to develop or maintain software. Although they relate to many different concerns (e.g., bug fixing, new feature development, architecture refactoring), few means are proposed to identify and classify these different kinds of issues, except for non mandatory labels that can be manually associated to them. This may lead to a lack of issue classification or to issue misclassification that may impact automatic issue management (planning, assignment) or issue-derived metrics. Automatic issue classification thus is a relevant topic for assisting backlog management. This paper proposes a binary classification solution for discriminating bug from non bug issues. This solution combines natural language processing (TF-IDF) and classification (multi-layer perceptron) techniques, selected after comparing commonly used solutions to classify issues. Moreover, hyper-parameters of the neural network are optimized using a genetic algorithm. The obtained results, as compared to existing works on a commonly used benchmark, show significant improvements on the F1 measure for all datasets.