ARTeMIS: Agent-based Rewriting and Test Case Management with Intelligent Supervision - Carvalho, Gabriel; Peralta, Andrés D.; Carvalho, André; Soares, Yan; Lima, Igor; Ferreira, Nikson; Melo, Hallyson (Anais do Simpósio Brasileiro de Qualidade de Software (SBQS); 2025: Anais do XXIV Simpósio Brasileiro de Qualidade de SoftwareDO - 10.5753/sbqs.2025.15058, 2025)
The quality of Test Case (TC) scripts is essential for the execution and automation of test scenarios on mobile devices. It is common to find TC components that are out of date, poorly written or inconsistent with documentation standards. In this work, we propose ARTeMIS, a modular Multi-Agent framework designed to automate the rewriting and validation of non-standardized TC components, integrating semantic retrieval, supervised classification, structured prompting and iterative rule-based validation using Large Language Models (LLMs). Our framework is capable of standardizing these three TC components: Summary, Initial Setup and Test Steps. ARTeMIS assigns each component to specialized agents for classification, rewriting and validation, enabling syntactic consistency and semantic accuracy with minimal human intervention. We performed experiments using, in addition to ARTeMIS, three other LLM-based techniques well established in the literature: Zero-Shot, Few-Shot and Retrieval-Augmented Generation (RAG). Our experiments demonstrated the feasibility and extensibility of our approach, which achieved a higher accuracy compared to the other techniques, highlighting the potential of agent-based architectures to standardize and automate TCs in continuous industrial testing environments.