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
ISSTA '04 Proceedings of the 2004 ACM SIGSOFT international symposium on Software testing and analysis
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
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
Software engineering research: from cradle to grave
Proceedings of the the 6th joint meeting of the European software engineering conference and the ACM SIGSOFT symposium on The foundations of software engineering
How to measure success of fault prediction models
Fourth international workshop on Software quality assurance: in conjunction with the 6th ESEC/FSE joint meeting
Predicting accurate and actionable static analysis warnings: an experimental approach
Proceedings of the 30th international conference on Software engineering
Comparing negative binomial and recursive partitioning models for fault prediction
Proceedings of the 4th international workshop on Predictor models in software engineering
Using sensitivity analysis to create simplified economic models for regression testing
ISSTA '08 Proceedings of the 2008 international symposium on Software testing and analysis
Comparing methods to identify defect reports in a change management database
DEFECTS '08 Proceedings of the 2008 workshop on Defects in large software systems
Empirical Software Engineering
Not all classes are created equal: toward a recommendation system for focusing testing
Proceedings of the 2008 international workshop on Recommendation systems for software engineering
Data mining source code for locating software bugs: A case study in telecommunication industry
Expert Systems with Applications: An International Journal
Progress in Automated Software Defect Prediction
HVC '08 Proceedings of the 4th International Haifa Verification Conference on Hardware and Software: Verification and Testing
Revisiting the evaluation of defect prediction models
PROMISE '09 Proceedings of the 5th International Conference on Predictor Models in Software Engineering
On the relative value of cross-company and within-company data for defect prediction
Empirical Software Engineering
Journal of Systems and Software
Comparing the effectiveness of several modeling methods for fault prediction
Empirical Software Engineering
What can fault prediction do for you?
TAP'08 Proceedings of the 2nd international conference on Tests and proofs
Better, faster, and cheaper: what is better software?
Proceedings of the 6th International Conference on Predictive Models in Software Engineering
Review: Software fault prediction: A literature review and current trends
Expert Systems with Applications: An International Journal
A framework for defect prediction in specific software project contexts
CEE-SET'08 Proceedings of the Third IFIP TC 2 Central and East European conference on Software engineering techniques
Are popular classes more defect prone?
FASE'10 Proceedings of the 13th international conference on Fundamental Approaches to Software Engineering
Software defect prediction using fuzzy support vector regression
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part II
Regularities in learning defect predictors
PROFES'10 Proceedings of the 11th international conference on Product-Focused Software Process Improvement
Evaluating defect prediction approaches: a benchmark and an extensive comparison
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
Influence of confirmation biases of developers on software quality: an empirical study
Software Quality Control
Improving scenario testing process by adding value-based prioritization: an industrial case study
Proceedings of the 2013 International Conference on Software and System Process
Comparative study on effectiveness of standard bug prediction approaches
Proceedings of the 5th IBM Collaborative Academia Research Exchange Workshop
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This research investigates ways of predicting which files would be most likely to contain large numbers of faults in the next release of a large industrial software system. Previous work involved making predictions using several different models ranging from a simple, fully-automatable model (the LOC model) to several different variants of a negative binomial regression model that were customized for the particular software system under study. Not surprisingly, the custom models invariably predicted faults more accurately than the simple model. However, development of customized models requires substantial time and analytic effort, as well as statistical expertise. We now introduce new, more sophisticated models that yield more accurate predictions than the earlier LOC model, but which nonetheless can be fully automated. We also extend our earlier research by presenting another large-scale empirical study of the value of these prediction models, using a new industrial software system over a nine year period.