Automated Detection of Refactoring Candidates in Behavior-Driven Development Test Suites Using Machine Learning
Researchers developed a machine learning system to automatically identify duplicated step sequences in Behavior-Driven Development (BDD) test suites and classify them by refactoring pattern. The study analyzed 339 repositories containing over 5 million step sequences and found that an XGBoost classifier outperformed rule-based and LLM-based approaches. The work addresses a practical software engineering problem by automating the detection of code duplication patterns that developers currently identify manually.
A research team created an automated pipeline to detect and rank recurring step subsequences in BDD test suites—specifically Gherkin-format tests—that are candidates for refactoring. Using a corpus of 339 repositories, they applied paraphrase-robust clustering (SBERT/UMAP/HDBSCAN) to identify 692,020 unique recurring patterns from 5.3 million total slices. Three human annotators labeled a stratified sample of 200 slices against a written rubric, achieving moderate agreement on extraction-worthiness (Fleiss' kappa = 0.56) and stronger agreement on refactoring mechanism (kappa = 0.79). An XGBoost classifier trained on this labeled data achieved an out-of-fold F1 score of 0.891, significantly outperforming both a tuned rule baseline (F1 = 0.836) and two open-weight LLM judges (best F1 = 0.728). The analysis found that 75% of scenarios contained within-file refactoring candidates, 59.5% contained within-repository reusable-scenario candidates, and 11.7% contained cross-organizational shared-step candidates. The authors released their pipeline, classifier predictions, labeled pool, and annotation rubric under an open-source license.
What's missing
The study's inter-rater agreement on extraction-worthiness (kappa = 0.56) falls in the 'moderate' range, suggesting some subjectivity in the labeling task that may limit classifier generalization. The paper does not discuss how the approach handles language-specific variations in Gherkin syntax across different teams or organizations, nor does it address computational scalability for repositories significantly larger than those in the study.
What different sources said
- arXiv cs.CLCenter
Given, When, Then, Again: Mining Subscenario Refactoring Candidates in Behaviour-Driven Test Suites with ML Classifiers and LLM-Judge Baselines
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