Software reliability: measurement, prediction, application
Software reliability: measurement, prediction, application
Handbook of software reliability engineering
Handbook of software reliability engineering
Queueing networks and Markov chains: modeling and performance evaluation with computer science applications
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
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
Predicting Defects for Eclipse
PROMISE '07 Proceedings of the Third International Workshop on Predictor Models in Software Engineering
A User-Oriented Software Reliability Model
IEEE Transactions on Software Engineering
An initial study of the growth of eclipse defects
Proceedings of the 2008 international working conference on Mining software repositories
Classifying Software Changes: Clean or Buggy?
IEEE Transactions on Software Engineering
On the Distribution of Software Faults
IEEE Transactions on Software Engineering
Quality Prediction of Service Compositions through Probabilistic Model Checking
QoSA '08 Proceedings of the 4th International Conference on Quality of Software-Architectures: Models and Architectures
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
A Hierarchical Reliability Model of Service-Based Software System
COMPSAC '09 Proceedings of the 2009 33rd Annual IEEE International Computer Software and Applications Conference - Volume 01
ICSM '10 Proceedings of the 2010 IEEE International Conference on Software Maintenance
Proceedings of the 34th 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
Hi-index | 0.00 |
During software maintenance, a large number of defects could be discovered and reported. A defect can enter many states during its lifecycle, such as NEW, ASSIGNED, and RESOLVED. The ability to predict the number of defects at each state can help project teams better evaluate and plan maintenance activities. In this paper, we present BugStates, a method for predicting defect numbers at each state based on defect state transition models. In our method, we first construct defect state transition models using historical data. We then derive a stability metric from the transition models to measure a project's defect-fixing performance. For projects with stable defect-fixing performance, we show that we can apply Markovian method to predict the number of defects at each state in future based on the state transition model. We evaluate the effectiveness of BugStates using six open source projects and the results are promising. For example, when predicting defect numbers at each state in December 2010 using data from July 2009 to June 2010, the absolute errors for all projects are less than 28. In general, BugStates also outperforms other related methods.