The Detection of Fault-Prone Programs
IEEE Transactions on Software Engineering
C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
Predicting Fault-Prone Software Modules in Telephone Switches
IEEE Transactions on Software Engineering
Predicting Fault Incidence Using Software Change History
IEEE Transactions on Software Engineering
The Mythical Man-Month: Essays on Softw
The Mythical Man-Month: Essays on Softw
Machine Learning
Classification and evaluation of defects in a project retrospective
Journal of Systems and Software
Software Faults in Evolving a Large, Real-Time System: a Case Study
ESEC '93 Proceedings of the 4th European Software Engineering Conference on Software Engineering
Reconstructing Ownership Architectures To Help Understand Software Systems
IWPC '99 Proceedings of the 7th International Workshop on Program Comprehension
Estimation of Software Defects Fix Effort Using Neural Networks
COMPSAC '04 Proceedings of the 28th Annual International Computer Software and Applications Conference - Workshops and Fast Abstracts - Volume 02
Software Defect Association Mining and Defect Correction Effort Prediction
IEEE Transactions on Software Engineering
Proceedings of the 28th international conference on Software engineering
Using Decision Trees to Predict the Certification Result of a Build
ASE '06 Proceedings of the 21st IEEE/ACM International Conference on Automated Software Engineering
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
Predicting bug-fixing time: an empirical study of commercial software projects
Proceedings of the 2013 International Conference on Software Engineering
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Background: Bug fixing lies at the core of most software maintenance efforts. Most prior studies examine the effort needed to fix a bug (fix-effort). However, the effort needed to fix a bug may not correlate with the calendar time needed to fix it (fix-time). For example, the fix-time for bugs with low fix-effort may be long if they are considered to be of low priority. Aims: We study the fix-time for bugs in large open source projects. Method: We study the fix-time along three dimensions: (1) the location of the bug (e.g., which component), (2) the reporter of the bug, and (3) the description of the bug. Using these three dimensions and their associated attributes, we examine the fix-time for bugs in two large open source projects: Eclipse and Mozilla, using a random forest classifier. Results: We show that we can correctly classify ~65% of the time the fix-time for bugs in these projects. We perform a sensitivity analysis to identify the most important attributes in each dimension. We find that the time of the filing of a bug and its location are the most important attributes in the Mozilla project for determining the fix-time of a bug. On the other hand, the fix-time in the Eclipse project is highly dependant on the severity of the bug. Surprisingly, the priority of the bug is not an important attribute when determining the fix-time for a bug in both projects. Conclusion: Attributes affecting the fix-time vary between projects and vary over time within the same project.