Comparative Analysis of Machine Learning-Based Software Defect Prediction in Object-Oriented and Structured Paradigms Using Apache Camel and Redis Datasets
DOI:
https://doi.org/10.52436/1.jutif.2026.7.1.5315Keywords:
Empirical Software Engineering, Machine Learning, Object-Oriented, Programming Paradigm, Software Defect Prediction, Software Metrics, StructuredAbstract
Software Defect Prediction (SDP) is a crucial component of software engineering aimed at improving quality and testing efficiency. However, the majority of SDP research often overlooks the fundamental influence of the programming paradigm on the nature and causes of defects. This study presents a comparative analysis to identify the most influential software metrics for predicting defects across two distinct paradigms: Object-Oriented (OOP) and Structured. To ensure modern relevance and reproducibility, we constructed two new datasets from large-scale, open-source projects: Apache Camel (Java) for OOP and Redis (C) for Structured which exhibited realistic defect rates of 14.4% and 21.8%, respectively. The dataset creation process involved mining Git repositories for defect labeling and automated metric extraction using the CK and Lizard tools. Correlation analysis and baseline modeling using Random Forest revealed significant differences between the paradigms. In the OOP system, dominant defect predictors were related to the complexity of the class interface and features (e.g., uniqueWordsQty, totalMethodsQty, WMC, CBO). Conversely, defects in the structured system were strongly correlated with size and algorithmic complexity (e.g., file_tokens, file_loc, file_ccn_sum). Although the baseline models performed well (ROC–AUC = 0.82–0.87), the significant class imbalance resulted in low recall (44–50%). This motivates the need for more context aware approaches. These findings underscore that effective SDP strategies must be tailored to the underlying programming paradigm.
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