Software errors and complexity: an empirical investigation0
Communications of the ACM
Predicting Fault Incidence Using Software Change History
IEEE Transactions on Software Engineering
Software evolution: code delta and code churn
Journal of Systems and Software - Special issue on software maintenance
Elements of Software Science (Operating and programming systems series)
Elements of Software Science (Operating and programming systems series)
Quantitative Analysis of Faults and Failures in a Complex Software System
IEEE Transactions on Software Engineering
Inference for the Generalization Error
Machine Learning
What We Have Learned About Fighting Defects
METRICS '02 Proceedings of the 8th International Symposium on Software Metrics
SEW '02 Proceedings of the 27th Annual NASA Goddard Software Engineering Workshop (SEW-27'02)
Code Churn: A Measure for Estimating the Impact of Code Change
ICSM '98 Proceedings of the International Conference on Software Maintenance
METRICS '03 Proceedings of the 9th International Symposium on Software Metrics
Algorithms for estimating relative importance in networks
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
ISSTA '04 Proceedings of the 2004 ACM SIGSOFT international symposium on Software testing and analysis
Introduction to Machine Learning (Adaptive Computation and Machine Learning)
Introduction to Machine Learning (Adaptive Computation and Machine Learning)
Static analysis tools as early indicators of pre-release defect density
Proceedings of the 27th international conference on Software engineering
Software Defect Association Mining and Defect Correction Effort Prediction
IEEE Transactions 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
Jazz and the Eclipse Way of Collaboration
IEEE Software
IEEE Transactions on Software Engineering
Fault Prediction using Early Lifecycle Data
ISSRE '07 Proceedings of the The 18th IEEE International Symposium on Software Reliability
Proceedings of the 30th international conference on Software engineering
Predicting defects using network analysis on dependency graphs
Proceedings of the 30th international conference on Software engineering
Implications of ceiling effects in defect predictors
Proceedings of the 4th international workshop on Predictor models in software engineering
IEEE Transactions on Software Engineering
Can developer-module networks predict failures?
Proceedings of the 16th ACM SIGSOFT International Symposium on Foundations of software engineering
Predicting failures with developer networks and social network analysis
Proceedings of the 16th ACM SIGSOFT International Symposium on Foundations of software engineering
Cost Curve Evaluation of Fault Prediction Models
ISSRE '08 Proceedings of the 2008 19th International Symposium on Software Reliability Engineering
PROMISE '09 Proceedings of the 5th International Conference on Predictor Models in Software Engineering
Predicting build failures using social network analysis on developer communication
ICSE '09 Proceedings of the 31st International Conference on Software Engineering
Merits of using repository metrics in defect prediction for open source projects
FLOSS '09 Proceedings of the 2009 ICSE Workshop on Emerging Trends in Free/Libre/Open Source Software Research and Development
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Reducing false alarms in software defect prediction by decision threshold optimization
ESEM '09 Proceedings of the 2009 3rd International Symposium on Empirical Software Engineering and Measurement
Detection of Gene Orthology Based on Protein-Protein Interaction Networks
BIBM '09 Proceedings of the 2009 IEEE International Conference on Bioinformatics and Biomedicine
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People are the most important pillar of software development process. It is critical to understand how they interact with each other and how these interactions affect the quality of the end product in terms of defects. In this research we propose to include a new set of metrics, a.k.a. social network metrics on issue repositories in predicting defects. Social network metrics on issue repositories has not been used before to predict defect proneness of a software product. To validate our hypotheses we used two datasets, development data of IBM1 Rational ® Team Concert™ (RTC) and Drupal, to conduct our experiments. The results of the experiments revealed that compared to other set of metrics such as churn metrics using social network metrics on issue repositories either considerably decreases high false alarm rates without compromising the detection rates or considerably increases low prediction rates without compromising low false alarm rates. Therefore we recommend practitioners to collect social network metrics on issue repositories since people related information is a strong indicator of past patterns in a given team.