Software errors and complexity: an empirical investigation0
Communications of the ACM
A Validation of Object-Oriented Design Metrics as Quality Indicators
IEEE Transactions on Software Engineering
Predicting Fault-Prone Software Modules in Telephone Switches
IEEE Transactions on Software Engineering
Investigating quality factors in object-oriented designs: an industrial case study
Proceedings of the 21st international conference on Software engineering
Predicting Fault Incidence Using Software Change History
IEEE Transactions on Software Engineering
Software Engineering Economics
Software Engineering Economics
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
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
Prediction of Fault-proneness at Early Phase in Object-Oriented Development
ISORC '99 Proceedings of the 2nd IEEE International Symposium on Object-Oriented Real-Time Distributed Computing
An introduction to variable and feature selection
The Journal of Machine Learning Research
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
Predicting fault-prone components in a java legacy system
Proceedings of the 2006 ACM/IEEE international symposium on Empirical software engineering
Data Mining Static Code Attributes to Learn Defect Predictors
IEEE Transactions on Software Engineering
Empirical Analysis of Object-Oriented Design Metrics for Predicting High and Low Severity Faults
IEEE Transactions on Software Engineering
Predicting Faults from Cached History
ICSE '07 Proceedings of the 29th international conference on Software Engineering
Using Developer Information as a Factor for Fault Prediction
ICSEW '07 Proceedings of the 29th International Conference on Software Engineering Workshops
A Replicated Quantitative Analysis of Fault Distributions in Complex Software Systems
IEEE Transactions on Software Engineering
Using Software Dependencies and Churn Metrics to Predict Field Failures: An Empirical Case Study
ESEM '07 Proceedings of the First International Symposium on Empirical Software Engineering and Measurement
Predicting defects using network analysis on dependency graphs
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
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
Validation of network measures as indicators of defective modules in software systems
PROMISE '09 Proceedings of the 5th International Conference on Predictor Models in Software Engineering
Predicting faults using the complexity of code changes
ICSE '09 Proceedings of the 31st International Conference on Software Engineering
Does calling structure information improve the accuracy of fault prediction?
MSR '09 Proceedings of the 2009 6th IEEE International Working Conference on Mining Software Repositories
IBM Systems Journal
Comparing the effectiveness of several modeling methods for fault prediction
Empirical Software Engineering
Studying the impact of dependency network measures on software quality
ICSM '10 Proceedings of the 2010 IEEE International Conference on Software Maintenance
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Previous studies have shown that software code attributes, such as lines of source code, and history information, such as the number of code changes and the number of faults in prior releases of software, are useful for predicting where faults will occur. In this study of two large industrial software systems, we investigate the effectiveness of adding information about calling structure to fault prediction models. Adding calling structure information to a model based solely on non-calling structure code attributes modestly improved prediction accuracy. However, the addition of calling structure information to a model that included both history and non-calling structure code attributes produced no improvement.