Beyond Green Tests: Removing Smells From Natural Language Tests - Aranda III, Manoel; Ribeiro, Márcio (Anais do Simpósio Brasileiro de Qualidade de Software (SBQS); 2025: Anais do XXIV Simpósio Brasileiro de Qualidade de Software, 2025)
Test smells signal design flaws in tests, harming maintainability and reliability. While automated test smells are well-studied, natural language test smells remain underexplored. Prior work identified 13 such smells but lacked systematic removal strategies and automated tools.We bridge this gap by presenting a catalog of transformations for seven key natural language test smells and a NLP-based tool for automated detection and correction. We evaluated our approach through a survey of 15 professionals and empirical analysis of Ubuntu OS test cases. Results show high professional acceptance (91.43%) and strong tool precision (83.70% F-Measure). Our work is the first to systematically address natural language test smell removal.