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Paper in IEEE Digital Library

Change Log

When	What
September 18th, 2015	Donated by Shaohua Wang

Reference

Studies who have been using the data (in any form) are required to include the following reference:

@inproceedings{wang2013improving,
  title={Improving bug localization using correlations in crash reports},
  author={Wang, Shaohua and Khomh, Foutse and Zou, Ying},
  booktitle={Mining Software Repositories (MSR), 2013 10th IEEE Working Conference on},
  pages={247--256},
  year={2013},
  organization={IEEE}
}

About the Data

Overview of Data

This data set contains 213k Eclipse bug reports filed between Oct 2001 and Dec 2007.

Attribute Information

Automatic Problem Reporting Tools, Bug Correlation, Bug Localization, Crash Reports, Crashes, Stack Traces

Paper Abstract

Nowadays, many software organizations rely on automatic problem reporting tools to collect crash reports directly from users’ environments. These crash reports are later grouped together into crash types. Usually, developers prioritize crash types based on the number of crash reports and file bugs for the top crash types. Because a bug can trigger a crash in different usage scenarios, different crash types are sometimes related to a same bug. Two bugs are correlated when the occurrence of one bug causes the other bug to occur. We refer to a group of crash types related to identical or correlated bugs, as a crash correlation group. In this paper, we propose three rules to identify correlated crash types automatically. We also propose an algorithm to locate and rank buggy files using crash correlation groups. Through an empirical study on Firefox and Eclipse, we show that the three rules can identify crash correlation groups with a precision of 100% and a recall of 90% for Firefox and a precision of 79% and a recall of 65% for Eclipse. On the top three buggy file candidates, the proposed bug localization algorithm achieves a recall of 62% and a precision of 42% for Firefox and a recall of 52% and a precision of 50% for Eclipse. On the top 10 buggy file candidates, the recall increases to 92% for Firefox and 90% for Eclipse. Developers can combine the proposed crash correlation rules with the new bug localization algorithm to identify and fix correlated crash types all together.

Data Categories

Eclipse Bug