Towards Better Summarizing Bug Reports with Crowdsourcing Elicited Attributes

TL;DR

This paper introduces a crowdsourcing-based method to construct effective attributes for bug report summarization, leading to a new supervised algorithm that outperforms existing methods on large-scale datasets.

Contribution

It proposes Crowd-Attribute for attribute construction from crowdsourced data and develops LRCA, a new supervised algorithm for bug report summarization.

Findings

LRCA outperforms state-of-the-art algorithms.

Constructed 11 new attributes using Crowd-Attribute.

Effective summarization achieved on large-scale datasets.

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 infer new effective attributes from the crowdgenerated data in 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.