A case study in root cause defect analysis
Proceedings of the 22nd international conference 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
Classification and evaluation of defects in a project retrospective
Journal of Systems and Software
Quantitative Analysis of Faults and Failures in a Complex Software System
IEEE Transactions on Software Engineering
Understanding and predicting effort in software projects
Proceedings of the 25th International Conference on Software Engineering
ISSTA '04 Proceedings of the 2004 ACM SIGSOFT international symposium on Software testing and analysis
Empirical evaluation of defect projection models for widely-deployed production software systems
Proceedings of the 12th ACM SIGSOFT twelfth international symposium on Foundations of software engineering
Quality-Evaluation Models and Measurements
IEEE Software
Proceedings of the 2006 international conference on Empirical software engineering issues: critical assessment and future directions
Towards a model to support in silico studies of software evolution
Proceedings of the ACM-IEEE international symposium on Empirical software engineering and measurement
Software verification process improvement proposal using six sigma
PROFES'07 Proceedings of the 8th international conference on Product-Focused Software Process Improvement
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This paper provides a study of several process metrics of an industrial large-scale embedded software system, the Lucent product Lambda-UniteTM MSS. This product is an evolutionary hardware/software system for the metropolitan and wide-area transmission and switching market. An analysis of defect data is performed, including and comparing all major (i.e. feature) releases till end of 2004. Several defect metrics on file-level are defined and analyzed, as basis for a defect prediction model. Main analysis results include the following. Faults and code size per file show only a weak correlation. Portion of faulty files per release tend to decrease across releases. Size and error-proneness in previous release alone is not a good predictor of a file's faults per release. Customer-found defects are strongly correlated with pre-delivery defects found per subsystem. These results are being compared to a recent similar study of fault distributions; the differences are significant.