Two case studies of open source software development: Apache and Mozilla
ACM Transactions on Software Engineering and Methodology (TOSEM)
How long did it take to fix bugs?
Proceedings of the 2006 international workshop on Mining software repositories
Supporting change request assignment in open source development
Proceedings of the 2006 ACM symposium on Applied computing
Predicting Eclipse Bug Lifetimes
MSR '07 Proceedings of the Fourth International Workshop on Mining Software Repositories
Proceedings of the twenty-second IEEE/ACM international conference on Automated software engineering
Proceedings of the 16th ACM SIGSOFT International Symposium on Foundations of software engineering
Cross-project defect prediction: a large scale experiment on data vs. domain vs. process
Proceedings of the the 7th joint meeting of the European software engineering conference and the ACM SIGSOFT symposium on The foundations of software engineering
Characterizing and predicting which bugs get fixed: an empirical study of Microsoft Windows
Proceedings of the 32nd ACM/IEEE International Conference on Software Engineering - Volume 1
Predicting the fix time of bugs
Proceedings of the 2nd International Workshop on Recommendation Systems for Software Engineering
Fine-grained incremental learning and multi-feature tossing graphs to improve bug triaging
ICSM '10 Proceedings of the 2010 IEEE International Conference on Software Maintenance
Empirical validation of human factors in predicting issue lead time in open source projects
Proceedings of the 7th International Conference on Predictive Models in Software Engineering
Predicting bug-fixing time: an empirical study of commercial software projects
Proceedings of the 2013 International Conference on Software Engineering
The eclipse and mozilla defect tracking dataset: a genuine dataset for mining bug information
Proceedings of the 10th Working Conference on Mining Software Repositories
Understanding the meaning of bug attributes and prediction models
Proceedings of the 5th IBM Collaborative Academia Research Exchange Workshop
Topic-based, time-aware bug assignment
ACM SIGSOFT Software Engineering Notes
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Predicting bug-fix time is useful in several areas of software evolution, such as predicting software quality or coordinating development effort during bug triaging. Prior work has proposed bug-fix time prediction models that use various bug report attributes (e.g., number of developers who participated in fixing the bug, bug severity, number of patches, bug-opener's reputation) for estimating the time it will take to fix a newly-reported bug. In this paper we take a step towards constructing more accurate and more general bug-fix time prediction models by showing how existing models fail to validate on large projects widely-used in bug studies. In particular, we used multivariate and univariate regression testing to test the prediction significance of existing models on 512,474 bug reports from five open source projects: Eclipse, Chrome and three products from the Mozilla project (Firefox, Seamonkey and Thunderbird). The results of our regression testing indicate that the predictive power of existing models is between 30% and 49% and that there is a need for more independent variables (attributes) when constructing a prediction model. Additionally, we found that, unlike in prior recent studies on commercial software, in the projects we examined there is no correlation between bug-fix likelihood, bug-opener's reputation and the time it takes to fix a bug. These findings indicate three open research problems: (1) assessing whether prioritizing bugs using bug-opener's reputation is beneficial, (2) identifying attributes which are effective in predicting bug-fix time, and (3) constructing bug-fix time prediction models which can be validated on multiple projects.