Improving bug localization using correlations in crash reports

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.

[1]  R. Yin.  Case Study Research: Design and Methods , 1984 .

[2]  Mayur Naik,et al.  From symptom to cause: localizing errors in counterexample traces , 2003, POPL '03.

[3]  Michael I. Jordan,et al.  Scalable statistical bug isolation , 2005, PLDI '05.

[4]  David J. Spiegelhalter,et al.  Machine Learning, Neural and Statistical Classification , 2009 .

[5]  J. Kruskal.  An Overview of Sequence Comparison: Time Warps, String Edits, and Macromolecules , 1983 .

[6]  Andreas Zeller,et al.  When do changes induce fixes? , 2005, ACM SIGSOFT Softw. Eng. Notes.

[7]  Rahul Premraj,et al.  Do stack traces help developers fix bugs? , 2010, 2010 7th IEEE Working Conference on Mining Software Repositories (MSR 2010).

[8]  Foutse Khomh,et al.  An Entropy Evaluation Approach for Triaging Field Crashes: A Case Study of Mozilla Firefox , 2011, 2011 18th Working Conference on Reverse Engineering.

[9]  Foutse Khomh,et al.  Classifying field crash reports for fixing bugs: A case study of Mozilla Firefox , 2011, 2011 27th IEEE International Conference on Software Maintenance (ICSM).

[10]  Nachiappan Nagappan,et al.  Crash graphs: An aggregated view of multiple crashes to improve crash triage , 2011, 2011 IEEE/IFIP 41st International Conference on Dependable Systems & Networks (DSN).

[11]  W. Campbell,et al.  THE UNIVERSITY OF TEXAS AT DALLAS , 2004 .

[12]  Bin Wang,et al.  Automated support for classifying software failure reports , 2003, 25th International Conference on Software Engineering, 2003. Proceedings..

[13]  Jiawei Han,et al.  BIDE: efficient mining of frequent closed sequences , 2004, Proceedings. 20th International Conference on Data Engineering.

[14]  Mary Lou Soffa,et al.  Path-based fault correlations , 2010, FSE '10.

[15]  John T. Stasko,et al.  Visualization of test information to assist fault localization , 2002, ICSE '02.

[16]  Mary Jean Harrold,et al.  Empirical evaluation of the tarantula automatic fault-localization technique , 2005, ASE.

[17]  Thomas Zimmermann,et al.  Extracting structural information from bug reports , 2008, MSR '08.

[18]  Sheng Ma,et al.  Automated Problem Determination Using Call-Stack Matching , 2005, Journal of Network and Systems Management.

[19]  Sooyong Park,et al.  Which Crashes Should I Fix First?: Predicting Top Crashes at an Early Stage to Prioritize Debugging Efforts , 2011, IEEE Transactions on Software Engineering.

[20]  Ying Zou,et al.  Visualizing the Results of Field Testing , 2010, 2010 IEEE 18th International Conference on Program Comprehension.

[21]  Latifur Khan,et al.  Software Fault Localization Using N-gram Analysis , 2008, WASA.

[22]  William G. Griswold,et al.  Foundations of Software Engineering (FSE-10) , 2002 .