Proceedings of the 28th international conference on Software engineering
How long did it take to fix bugs?
Proceedings of the 2006 international workshop on Mining software repositories
How Long Will It Take to Fix This Bug?
MSR '07 Proceedings of the Fourth International Workshop on Mining Software Repositories
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
An approach to detecting duplicate bug reports using natural language and execution information
Proceedings of the 30th international conference on Software engineering
IEEE Transactions on Software Engineering
Proceedings of the 16th ACM SIGSOFT International Symposium on Foundations of software engineering
Fair and balanced?: bias in bug-fix datasets
Proceedings of the the 7th joint meeting of the European software engineering conference and the ACM SIGSOFT symposium on The foundations of software engineering
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Bug-fix time prediction models: can we do better?
Proceedings of the 8th Working Conference on Mining Software Repositories
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
A quantitative measure for preventive maintenance in software
ACM SIGSOFT Software Engineering Notes
Factors characterizing reopened issues: a case study
Proceedings of the 8th International Conference on Predictive Models in Software Engineering
Recommender systems for manual testing: deciding how to assign tests in a test team
Proceedings of the ACM-IEEE international symposium on Empirical software engineering and measurement
Issue ownership activity in two large software projects
ACM SIGSOFT Software Engineering Notes
It's not a bug, it's a feature: how misclassification impacts bug prediction
Proceedings of the 2013 International Conference on Software Engineering
Predicting bug-fixing time: an empirical study of commercial software projects
Proceedings of the 2013 International Conference on Software Engineering
Will my patch make it? and how fast?: case study on the Linux kernel
Proceedings of the 10th Working Conference on Mining Software Repositories
The eclipse and mozilla defect tracking dataset: a genuine dataset for mining bug information
Proceedings of the 10th Working Conference on Mining Software Repositories
Is lines of code a good measure of effort in effort-aware models?
Information and Software Technology
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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Two important questions concerning the coordination of development effort are which bugs to fix first and how long it takes to fix them. In this paper we investigate empirically the relationships between bug report attributes and the time to fix. The objective is to compute prediction models that can be used to recommend whether a new bug should and will be fixed fast or will take more time for resolution. We examine in detail if attributes of a bug report can be used to build such a recommender system. We use decision tree analysis to compute and 10-fold cross validation to test prediction models. We explore prediction models in a series of empirical studies with bug report data of six systems of the three open source projects Eclipse, Mozilla, and Gnome. Results show that our models perform significantly better than random classification. For example, fast fixed Eclipse Platform bugs were classified correctly with a precision of 0.654 and a recall of 0.692. We also show that the inclusion of postsubmission bug report data of up to one month can further improve prediction models.