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.