Hadil Ben Amor | Searcharxiv

arXiv2026

MLmisFinder: A Specification and Detection Approach of Machine Learning Service Misuses

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.

cs.SE↗

arXiv2025

A Comprehensive Multi-Vocal Empirical Study of ML Cloud Service Misuses

Machine Learning (ML) models are widely used across various domains, including medical diagnostics and autonomous driving. To support this growth, cloud providers offer ML services to ease the integration of ML components in software systems. The evolving business requirements and the popularity of ML services have led practitioners of all skill levels to implement, and maintain ML service-based systems. However, they may not always adhere to optimal design and usage practices for ML cloud services, resulting in common misuse which could significantly degrade the quality of ML service-based systems and adversely affect their maintenance and evolution. Though much research has been conducted on ML service misuse, a consistent terminology and specification for these misuses remain absent. We therefore conduct in this paper a comprehensive, multi-vocal empirical study exploring the prevalence of ML cloud service misuses in practice. We propose a catalog of 20 ML cloud service misuses, most of which have not been studied in prior research. To achieve this, we conducted a) a systematic literature review of studies on ML misuses, b) a gray literature review of the official documentation provided by major cloud providers, c) an empirical analysis of a curated set of 377 ML service-based systems on GitHub, and d) a survey with 50 ML practitioners. Our results show that ML service misuses are common in both open-source projects and industry, often stemming from a lack of understanding of service capabilities, and insufficient documentation. This emphasizes the importance of ongoing education in best practices for ML services, which is the focus of this paper, while also highlighting the need for tools to automatically detect and refactor ML misuses.

cs.SE↗