Micro interaction metrics for defect prediction
Proceedings of the 19th ACM SIGSOFT symposium and the 13th European conference on Foundations of software engineering
ReBucket: a method for clustering duplicate crash reports based on call stack similarity
Proceedings of the 34th International Conference on Software Engineering
Predicting recurring crash stacks
Proceedings of the 27th IEEE/ACM International Conference on Automated Software Engineering
Predicting method crashes with bytecode operations
Proceedings of the 6th India Software Engineering Conference
Discovering, reporting, and fixing performance bugs
Proceedings of the 10th Working Conference on Mining Software Repositories
Improving bug localization using correlations in crash reports
Proceedings of the 10th Working Conference on Mining Software Repositories
Hi-index | 0.00 |
Many popular software systems automatically report failures back to the vendors, allowing developers to focus on the most pressing problems. However, it takes a certain period of time to assess which failures occur most frequently. In an empirical investigation of the Firefox and Thunderbird crash report databases, we found that only 10 to 20 crashes account for the large majority of crash reports; predicting these “top crashes” thus could dramatically increase software quality. By training a machine learner on the features of top crashes of past releases, we can effectively predict the top crashes well before a new release. This allows for quick resolution of the most important crashes, leading to improved user experience and better allocation of maintenance efforts.