Software errors and complexity: an empirical investigation0
Communications of the ACM
The Detection of Fault-Prone Programs
IEEE Transactions on Software Engineering
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
Does Code Decay? Assessing the Evidence from Change Management Data
IEEE Transactions on Software Engineering
The distribution of faults in a large industrial software system
ISSTA '02 Proceedings of the 2002 ACM SIGSOFT international symposium on Software testing and analysis
An empirical evaluation of fault-proneness models
Proceedings of the 24th International Conference on Software Engineering
Reexamining the Fault Density-Component Size Connection
IEEE Software
A Metrics Suite for Object Oriented Design
IEEE Transactions on Software Engineering
Quantitative Analysis of Faults and Failures in a Complex Software System
IEEE Transactions on Software Engineering
An Empirical Analysis of Fault Persistence Through Software Releases
ISESE '03 Proceedings of the 2003 International Symposium on Empirical Software Engineering
Robust Prediction of Fault-Proneness by Random Forests
ISSRE '04 Proceedings of the 15th International Symposium on Software Reliability Engineering
Predicting the Location and Number of Faults in Large Software Systems
IEEE Transactions on Software Engineering
Mining metrics to predict component failures
Proceedings of the 28th international conference on Software engineering
Looking for bugs in all the right places
Proceedings of the 2006 international symposium on Software testing and analysis
Predicting fault-prone components in a java legacy system
Proceedings of the 2006 ACM/IEEE international symposium on Empirical software engineering
Automating algorithms for the identification of fault-prone files
Proceedings of the 2007 international symposium on Software testing and analysis
Optimizing preventive service of software products
IBM Journal of Research and Development
Practical development of an Eclipse-based software fault prediction tool using Naive Bayes algorithm
Expert Systems with Applications: An International Journal
An investigation on the feasibility of cross-project defect prediction
Automated Software Engineering
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It would obviously be very valuable to know in advance which files in the next release of a large software system are most likely to contain the largest numbers of faults. This is true whether the goal is to validate the system by testing or formally verifying it, or by using some hybrid approach. To accomplish this, we developed negative binomial regression models and used them to predict the expected number of faults in each file of the next release of a system. The predictions are based on code characteristics and fault and modification history data. This paper discusses what we have learned from applying the model to several large industrial systems, each with multiple years of field exposure. It also discusses our success in making accurate predictions and some of the issues that had to be considered.