Extractive Summarization of Related Bug-fixing Comments in Support of Bug Repair

TL;DR

This paper proposes an extractive summarization approach for bug-fixing comments in developer discussions, combining sentiment analysis, TextRank, and VSM to improve relevance detection and summarization.

Contribution

It introduces a novel combined method integrating sentiment analysis, TextRank, and VSM for extracting relevant bug-fixing comments from lengthy discussion threads.

Findings

Bug-fixing comments tend to be positive.

Semantic relevance exists among cross-cutting discussion comments.

The combined approach outperforms baseline VSM in ranking performance.

Abstract

When developers investigate a new bug report, they search for similar previously fixed bug reports and discussion threads attached to them. These discussion threads convey important information about the behavior of the bug including relevant bug-fixing comments. Oftentimes, these discussion threads become extensively lengthy due to the severity of the reported bug. This adds another layer of complexity, especially if relevant bug-fixing comments intermingle with seemingly unrelated comments. To manually detect these relevant comments among various cross-cutting discussion threads can become a daunting task when dealing with high volume of bug reports. To automate this process, our focus is to initially extract and detect comments in the context of query relevance, the use of positive language, and semantic relevance. Then, we merge these comments in the form of a summary for easy understanding. Specifically, we combine Sentiment Analysis and the TextRank Model with the baseline Vector Space Model (VSM). Preliminary findings indicate that bug-fixing comments tend to be positive and there exists a semantic relevance with comments from other cross-cutting discussion threads. The results also indicate that our combined approach improves overall ranking performance against the baseline VSM.