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English(EN) Mining Subscenario Refactoring Opportunities in Behaviour-Driven Software Test Suites: ML Classifiers and LLM-Judge Baselines

ML分类器自动化重构BDD测试套件

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种自动识别和分类行为驱动开发（BDD）软件测试套件中重构机会的方法。他们的方法使用机器学习分类器，特别是eXtreme Gradient Boosting模型，来检测适合提取的重复步骤子序列。该分类器在识别这些重构机会方面优于基于规则的基线和大型语言模型裁判，为管理和改进测试套件的可维护性提供了更有效的方式。 AI

影响 自动化BDD测试套件的重构，可能提高软件开发效率和测试套件质量。

排序理由 该集群包含一篇学术论文，详细介绍了针对特定软件工程任务的新方法论和实验结果。[lever_c_demoted from research: ic=1 ai=0.7]

在 arXiv cs.CL 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

ML分类器自动化重构BDD测试套件

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该集群包含一篇学术论文，详细介绍了针对特定软件工程任务的新方法论和实验结果。[lever_c_demoted from research: ic=1 ai=0.7]
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报道来源 [1]

  1. arXiv cs.CL TIER_1 English(EN) · Muhammad Bilal ·

    在行为驱动软件测试套件中挖掘子场景重构机会：ML分类器与LLM-Judge基线

    Context. Behaviour-Driven Development (BDD) software test suites accumulate duplicated step subsequences. Three published refactoring patterns are available (within-file Background, within-repo reusable-scenario invocation, cross-organisational shared higher-level step), but no p…

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