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This paper introduces MLmisFinder, a novel approach for automatically detecting misuses of machine learning cloud services in software systems. The approach defines a metamodel to capture relevant data and employs rule-based algorithms to identify seven specific misuse types. Evaluation on 107 GitHub repositories demonstrates that MLmisFinder achieves high precision (96.7%) and recall (97%) in detecting misuses, significantly outperforming existing methods and revealing widespread misuse in areas like data drift monitoring.
Despite the ease of integrating ML cloud services, developers are widely misusing them, leading to quality and maintainability issues that MLmisFinder can now automatically detect with high accuracy.
Machine Learning (ML) cloud services, offered by leading providers such as Amazon, Google, and Microsoft, enable the integration of ML components into software systems without building models from scratch. However, the rapid adoption of ML services, coupled with the growing complexity of business requirements, has led to widespread misuses, compromising the quality, maintainability, and evolution of ML service-based systems. Though prior research has studied patterns and antipatterns in service-based and ML-based systems separately, automatic detection of ML service misuses remains a challenge. In this paper, we propose MLmisFinder, an automatic approach to detect ML service misuses in software systems, aiming to identify instances of improper use of ML services to help developers properly integrate ML components in ML service-based systems. We propose a metamodel that captures the data needed to detect misuses in ML service-based systems and apply a set of rule-based detection algorithms for seven misuse types. We evaluated MLmisFinder on 107 software systems collected from open-source GitHub repositories and compared it with a state-of-the-art baseline. Our results show that MLmisFinder effectively detects ML service misuses, achieving an average precision of 96.7\% and recall of 97\%, outperforming the state-of-the-art baseline. MLmisFinder also scaled efficiently to detect misuses across 817 ML service-based systems and revealed that such misuses are widespread, especially in areas such as data drift monitoring and schema validation.
