C4.5: programs for machine learning
C4.5: programs for machine learning
An empirical study of operating systems errors
SOSP '01 Proceedings of the eighteenth ACM symposium on Operating systems principles
How long did it take to fix bugs?
Proceedings of the 2006 international workshop on Mining software repositories
Proceedings of the 16th ACM SIGSOFT International Symposium on Foundations of software engineering
Mining software repositories for software change impact analysis: a case study
SBBD '08 Proceedings of the 23rd Brazilian symposium on Databases
Improving bug triage with bug tossing graphs
Proceedings of the the 7th joint meeting of the European software engineering conference and the ACM SIGSOFT symposium on The foundations of software engineering
IWSM '09 /Mensura '09 Proceedings of the International Conferences on Software Process and Product Measurement
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
Security versus performance bugs: a case study on Firefox
Proceedings of the 8th Working Conference on Mining Software Repositories
Bug-fix time prediction models: can we do better?
Proceedings of the 8th Working Conference on Mining Software Repositories
Improving efficiency in software maintenance
Proceedings of the 8th Working Conference on Mining Software Repositories
Studying the fix-time for bugs in large open source projects
Proceedings of the 7th International Conference on Predictive Models in Software Engineering
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
Empirical Software Engineering
Characterizing and predicting which bugs get reopened
Proceedings of the 34th International Conference on Software Engineering
Predicting defect numbers based on defect state transition models
Proceedings of the ACM-IEEE international symposium on Empirical software engineering and measurement
Predicting bug-fixing time: an empirical study of commercial software projects
Proceedings of the 2013 International Conference on Software Engineering
| Hi-index | 0.00 |
In non-trivial software development projects planning and allocation of resources is an important and difficult task. Estimation of work time to fix a bug is commonly used to support this process. This research explores the viability of using data mining tools to predict the time to fix a bug given only the basic information known at the beginning of a bug's lifetime. To address this question, a historical portion of the Eclipse Bugzilla database is used for modeling and predicting bug lifetimes. A bug history transformation process is described and several data mining models are built and tested. Interesting behaviours derived from the models are documented. The models can correctly predict up to 34.9% of the bugs into a discretized log scaled lifetime class.