Given, When, Then, Again: Mining Subscenario Refactoring Candidates in Behaviour-Driven Test Suites with ML Classifiers and LLM-Judge Baselines
Researchers have developed a novel method to identify and categorize refactoring opportunities within behavior-driven development (BDD) test suites. By employing machine learning classifiers and Large Language Model (LLM) judges, the system can detect recurring step subsequences, assess their suitability for extraction, and map them to specific refactoring patterns. This approach aims to automate the process of improving the maintainability and reusability of BDD test code across the public Gherkin ecosystem. AI
IMPACT Automates identification of reusable code patterns in software testing, potentially improving development efficiency.