An exploratory study on improving automated issue triage with attached screenshots

ABSTRACTIssue triage is a manual and time consuming process for both open and closed source software projects. Triagers first validate the issue reports and then find the appropriate developers or teams to solve them. In our industrial case, we automated the assignment part of the problem with a machine learning based approach. However, the automated system's average accuracy performance is 3% below the human triagers' performance. In our effort to improve our approach, we analyzed the incorrectly assigned issue reports and realized that many of them have attachments with them, which are mostly screenshots. Such issue reports generally have short descriptions compared to the ones without attachments, which we consider as one of the reasons for incorrect classification. In this study, we describe our proposed approach to include this new piece of information for issue triage and present the initial results.