Software reliability: measurement, prediction, application
Software reliability: measurement, prediction, application
Software Engineering Economics
Software Engineering Economics
Two case studies of open source software development: Apache and Mozilla
ACM Transactions on Software Engineering and Methodology (TOSEM)
Quantitative Analysis of Faults and Failures in a Complex Software System
IEEE Transactions on Software Engineering
Understanding and predicting effort in software projects
Proceedings of the 25th International Conference on Software Engineering
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
How long did it take to fix bugs?
Proceedings of the 2006 international workshop on Mining software repositories
Data Mining Static Code Attributes to Learn Defect Predictors
IEEE Transactions on Software Engineering
A Replicated Quantitative Analysis of Fault Distributions in Complex Software Systems
IEEE Transactions on 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
Classifying Software Changes: Clean or Buggy?
IEEE Transactions on Software Engineering
On the relative value of cross-company and within-company data for defect prediction
Empirical 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
"Not my bug!" and other reasons for software bug report reassignments
Proceedings of the ACM 2011 conference on Computer supported cooperative work
Data Mining: Practical Machine Learning Tools and Techniques
Data Mining: Practical Machine Learning Tools and Techniques
Bug-fix time prediction models: can we do better?
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
A study of term weighting schemes using class information for text classification
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Predicting defect numbers based on defect state transition models
Proceedings of the ACM-IEEE international symposium on Empirical software engineering and measurement
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For a large and evolving software system, the project team could receive many bug reports over a long period of time. It is important to achieve a quantitative understanding of bug-fixing time. The ability to predict bug-fixing time can help a project team better estimate software maintenance efforts and better manage software projects. In this paper, we perform an empirical study of bug-fixing time for three CA Technologies projects. We propose a Markov-based method for predicting the number of bugs that will be fixed in future. For a given number of defects, we propose a method for estimating the total amount of time required to fix them based on the empirical distribution of bug-fixing time derived from historical data. For a given bug report, we can also construct a classification model to predict slow or quick fix (e.g., below or above a time threshold). We evaluate our methods using real maintenance data from three CA Technologies projects. The results show that the proposed methods are effective.