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
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
A different view of fault prediction
COMPSAC-W'05 Proceedings of the 29th annual international conference on Computer software and applications conference
Using Developer Information as a Factor for Fault Prediction
PROMISE '07 Proceedings of the Third International Workshop on Predictor Models in Software Engineering
Automating algorithms for the identification of fault-prone files
Proceedings of the 2007 international symposium on Software testing and analysis
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
Proceedings of the 30th international conference on Software engineering
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
Adapting a fault prediction model to allow inter languagereuse
Proceedings of the 4th international workshop on Predictor models in software engineering
Exploring the relationship of history characteristics and defect count: an empirical study
DEFECTS '08 Proceedings of the 2008 workshop on Defects in large software systems
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
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
On the relative value of cross-company and within-company data for defect prediction
Empirical Software Engineering
Increasing diversity: Natural language measures for software fault prediction
Journal of Systems and Software
Information and Software Technology
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
Towards identifying software project clusters with regard to defect prediction
Proceedings of the 6th International Conference on Predictive Models in Software Engineering
Better, faster, and cheaper: what is better software?
Proceedings of the 6th International Conference on Predictive Models in Software Engineering
Information and Software Technology
Regularities in learning defect predictors
PROFES'10 Proceedings of the 11th international conference on Product-Focused Software Process Improvement
A topic-based approach for narrowing the search space of buggy files from a bug report
ASE '11 Proceedings of the 2011 26th IEEE/ACM International Conference on Automated 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
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We continue investigating the use of a negative binomial regression model to predict which files in a large industrial software system are most likely to contain many faults in the next release. A new empirical study is described whose subject is an automated voice response system. Not only is this system's functionality substantially different from that of the earlier systems we studied (an inventory system and a service provisioning system), it also uses a significantly different software development process. Instead of having regularly scheduled releases as both of the earlier systems did, this system has what are referred to as "continuous releases." We explore the use of three versions of the negative binomial regression model, as well as a simple lines-of-code based model, to make predictions for this system and discuss the differences observed from the earlier studies. Despite the different development process, the best version of the prediction model was able to identify, over the lifetime of the project, 20% of the system's files that contained, on average, nearly three quarters of the faults that were detected in the system's next releases.