Performance evaluation of medical expert systems using ROC curves
Computers and Biomedical Research
C4.5: programs for machine learning
C4.5: programs for machine learning
Safeware: system safety and computers
Safeware: system safety and computers
Software metrics (2nd ed.): a rigorous and practical approach
Software metrics (2nd ed.): a rigorous and practical approach
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Fast formal analysis of requirements via “Topoi Diagrams”
ICSE '01 Proceedings of the 23rd International Conference on Software Engineering
Software Testability: The New Verification
IEEE Software
The Case against Accuracy Estimation for Comparing Induction Algorithms
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Towards a Theory for Integration of Mathematical Verification and Empirical Testing
ASE '98 Proceedings of the 13th IEEE international conference on Automated software engineering
Operational anomalies as a cause of safety-critical requirements evolution
Journal of Systems and Software
METRICS '03 Proceedings of the 9th International Symposium on Software Metrics
Data Mining Static Code Attributes to Learn Defect Predictors
IEEE Transactions on Software Engineering
IEEE Transactions on Software Engineering
Implications of ceiling effects in defect predictors
Proceedings of the 4th international workshop on Predictor models in software engineering
Identifying poorly documented object oriented software components
International Journal of Hybrid Intelligent Systems
Exhaustive and heuristic search approaches for learning a software defect prediction model
Engineering Applications of Artificial Intelligence
Defect prediction from static code features: current results, limitations, new approaches
Automated Software Engineering
Review: Software fault prediction: A literature review and current trends
Expert Systems with Applications: An International Journal
The design of polynomial function-based neural network predictors for detection of software defects
Information Sciences: an International Journal
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Assessing software costs money and better assessment costs exponentially more money. Given finite budgets, assessment resources are typically skewed towards areas that are believed to be mission critical. This leaves blind spots: portions of the system that may contain defects which may be missed. Therefore, in addition to rigorously assessing mission critical areas, a parallel activity should sample the blind spots. This paper assesses defect detectors based on static code measures as a blind spot sampling method. In contrast to previous results, we find that such defect detectors yield results that are stable across many applications. Further, these detectors are inexpensive to use and can be tuned to the specifics of the current business situations.