Vocabulary and time based bug‐assignment: A recommender system for open‐source projects. (27th April 2020)

• Record Type: Journal Article
• Authors: Sajedi‐Badashian, Ali; Stroulia, Eleni
• Is Part Of: Software, practice & experience. Volume 50:Number 8(2020)
• Page Start: 1539
• Page End: 1564
• Publication Date: 2020-04-27
• Subjects: TF‐IDF -- bug‐assignment -- GitHub -- information retrieval -- software engineering; Computer software -- Periodicals; Computer programming -- Periodicals; Computer programs -- Periodicals; 005.3
• DOI: 10.1002/spe.2830
• Languages: English
• ISSNs: 0038-0644

Abstract

Summary: Bug‐assignment (BA), the task of ranking developers in terms of the relevance of their expertise to fix a new bug report is time consuming, which is why substantial attention has been paid to developing methods for automating it. In this article, we describe a new BA approach that relies on two key intuitions. Similar to traditional BA methods, our method constructs the expertise profile of project developers, based on the textual elements of the bugs they have fixed in the past; unlike traditional methods, however, our method considers only the programming keywords in these bug descriptions, relying on Stack Overflow as the vocabulary for these keywords. The second key intuition of our method is that recent expertise is more relevant than past expertise, which is why our method weighs the relevance of a developer's expertise based on how recently they have fixed a bug with keywords similar to the bug at hand. We evaluated our BA method using a dataset of 93k bug‐report assignments from 13 popular GitHub projects. In spite of its simplicity, our method predicts the assignee with high accuracy, outperforming state‐of‐the‐art methods.