Software errors and complexity: an empirical investigation0
Communications of the ACM
The Detection of Fault-Prone Programs
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
Machine Learning
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
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
An investigation of the effect of module size on defect prediction using static measures
PROMISE '05 Proceedings of the 2005 workshop on Predictor models in 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
On the use of calling structure information to improve fault prediction
Empirical Software Engineering
A learning-to-rank algorithm for constructing defect prediction models
IDEAL'12 Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning
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Two different software fault prediction models have been used to predict the N% of the files of a large software system that are likely to contain the largest numbers of faults. We used the same predictor variables in a negative binomial regression model and a recursive partitioning model, and compared their effectiveness on three large industrial software systems. The negative binomial model identified files that contain 76 to 93 percent of the faults, and recursive partitioning identified files that contain 68 to 85 percent.