A Combination of Classification and Summarization Techniques for Bug Report Summarization

Abstract

Well-written bug reports should encompass bug descriptions, reproduction steps, environment details, and solutions. Bug report summaries also need to include such information to be highly informative for developers. However, traditional bug summarization techniques only apply summarization techniques to bug reports, and the generated summaries do not contain such information in a balanced way. In this paper, we propose summarizing duplicate bug reports by including bug descriptions, reproduction steps, environment details, and solutions. For that, our approach combines a supervised classification approach with the pre-trained summarization model BART. Additionally, we performed comparative experiments to demonstrate the effectiveness of this new approach in comparison to existing Summary and Authorship datasets. The experiments reveal that our approach outperforms the state-of-the-art method, achieving a 5% and 7% higher F-score for the Summary and Authorship datasets. © 2023 Copyright for this paper by its authors.