Predicting defective software components from code complexity measures

The ability to predict defective modules can help us allocate limited quality assurance resources effectively and efficiently. In this paper, we propose a complexity-based 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 non-defective. 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.