An initial study of the growth of eclipse defects
Proceedings of the 2008 international working conference on Mining software repositories
Measuring design complexity of semantic web ontologies
Journal of Systems and Software
Can complexity, coupling, and cohesion metrics be used as early indicators of vulnerabilities?
Proceedings of the 2010 ACM Symposium on Applied Computing
Predicting vulnerable software components with dependency graphs
Proceedings of the 6th International Workshop on Security Measurements and Metrics
Which is the right source for vulnerability studies?: an empirical analysis on Mozilla Firefox
Proceedings of the 6th International Workshop on Security Measurements and Metrics
Using complexity, coupling, and cohesion metrics as early indicators of vulnerabilities
Journal of Systems Architecture: the EUROMICRO Journal
Dealing with noise in defect prediction
Proceedings of the 33rd International Conference on Software Engineering
A diagnostic reasoning approach to defect prediction
IEA/AIE'11 Proceedings of the 24th international conference on Industrial engineering and other applications of applied intelligent systems conference on Modern approaches in applied intelligence - Volume Part II
Sample-based software defect prediction with active and semi-supervised learning
Automated Software Engineering
Is this a bug or an obsolete test?
ECOOP'13 Proceedings of the 27th European conference on Object-Oriented Programming
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The ability to predict defective modules can help us allocate limited quality assurance resources effectively and efficiently. In this paper, we propose a complexitybased method for predicting defect-prone components. Our method takes three code-level complexity measures as input, namely Lines of Code, McCabe's Cyclomatic Complexity and Halstead's Volume, and classifies components as either defective or nondefective. We perform an extensive study of twelve classification models using the public NASA datasets. Cross-validation results show that our method can achieve good prediction accuracy. This study confirms that static code complexity measures can be useful indicators of component quality.