Towards Better Summarizing Bug Reports with Crowdsourcing Elicited Attributes

Abstract

Recent years have witnessed the growing demands for resolving numerous bug reports in software maintenance. Aiming to reduce the time testers/developers take in perusing bug reports, the task of bug report summarization has attracted a lot of research efforts in the literature. However, no systematic analysis has been conducted on attribute construction which heavily impacts the performance of supervised algorithms for bug report summarization. In this study, we first conduct a survey to reveal the existing methods for attribute construction in mining software repositories. Then, we propose a new method named Crowd-Attribute to construct new effective attributes by crowdsourcing and develop a new tool named Crowdsourcing Software Engineering Platform to facilitate this method. With Crowd-Attribute, we successfully construct 11 new attributes and propose a new supervised algorithm named Logistic Regression with Crowdsourced Attributes (LRCA). To evaluate the effectiveness of LRCA, we build a series of large scale data sets with 105,177 bug reports. Experiments over both the public data set SDS with 36 manually annotated bug reports and new large scale data sets demonstrate that LRCA can consistently outperform the state-of-the-art algorithms for bug report summarization.

Dataset

1

We automatically construct a large data set for bug report summarization, which is publicly available. The data set can be used to evaluate algorithms for bug report summarization.

The data set is consist of four projects, including Eclipse, Mozilla, Gnome, and KDE. You can download the data set from the below links Eclipse, Mozilla, Gnome, KDE.

2

To facilitate the research on Crowdsourcing, we share the source codes of our CSEP platform. Users can deploy the platform for attribute construction. Meanwhile, users can also customize the tool for different purposes.

You can download the platform from the below link CSEP Platform.

Supplemental Material

We provide a document to detail each phase of the Heuristic Construction Rules (HCRs), including:

• Explanation: The explanation explains the concept and general process of a phase.
• Rules: Rules describe the methods to manually apply HCRs by requesters.
• Example: The example gives a simple example in a phase.
• Suggestion: The suggestion provides tools and methods to completely or partially automate HCRs.

If you have any questions about the data, please contact "li1989(at)mail.dlut.edu.cn".