Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
Visualization of test information to assist fault localization
Proceedings of the 24th International Conference on Software Engineering
From symptom to cause: localizing errors in counterexample traces
POPL '03 Proceedings of the 30th ACM SIGPLAN-SIGACT symposium on Principles of programming languages
Automated support for classifying software failure reports
Proceedings of the 25th International Conference on Software Engineering
BIDE: Efficient Mining of Frequent Closed Sequences
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Scalable statistical bug isolation
Proceedings of the 2005 ACM SIGPLAN conference on Programming language design and implementation
MSR '05 Proceedings of the 2005 international workshop on Mining software repositories
Empirical evaluation of the tarantula automatic fault-localization technique
Proceedings of the 20th IEEE/ACM international Conference on Automated software engineering
Extracting structural information from bug reports
Proceedings of the 2008 international working conference on Mining software repositories
Software Fault Localization Using N-gram Analysis
WASA '08 Proceedings of the Third International Conference on Wireless Algorithms, Systems, and Applications
Proceedings of the eighteenth ACM SIGSOFT international symposium on Foundations of software engineering
IEEE Transactions on Software Engineering
Crash graphs: An aggregated view of multiple crashes to improve crash triage
DSN '11 Proceedings of the 2011 IEEE/IFIP 41st International Conference on Dependable Systems&Networks
An Entropy Evaluation Approach for Triaging Field Crashes: A Case Study of Mozilla Firefox
WCRE '11 Proceedings of the 2011 18th Working Conference on Reverse Engineering
Classifying field crash reports for fixing bugs: A case study of Mozilla Firefox
ICSM '11 Proceedings of the 2011 27th IEEE International Conference on Software Maintenance
Hi-index | 0.00 |
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.