FlaKat：一个基于机器学习的 Flaky 测试分类框架
FlaKat: A Machine Learning-Based Categorization Framework for Flaky Tests
AI总结：
本文提出 FlaKat 框架，利用机器学习分类器和采样技术预测 flaky 测试的根因类别，并提出基于信息论的 FDC 指标验证分类效果。
摘要：
Flaky 测试能够在软件系统未发生任何变更的情况下非确定性地通过或失败。这类测试经常被开发者遇到，并削弱测试套件的可信度。当前最先进的研究将机器学习解决方案纳入 flaky 测试检测，并取得了相当不错的准确率。此外，大多数自动化 flaky 测试修复方案都是针对特定类型的 flaky 测试设计的。本研究工作提出了一个名为 FlaKat 的新型分类框架，该框架使用机器学习分类器，对给定 flaky 测试反映其根本原因的类别进行快速而准确的预测。研究采用采样技术，以解决 International Dataset of Flaky Test（IDoFT）中 flaky 测试类别之间的不平衡问题。论文还提出了一种名为 Flakiness Detection Capacity（FDC）的新评估指标，用于从信息论角度衡量分类器的准确率，并提供了其有效性的证明。关于哪一个分类器能够产生最佳 flakiness 分类，最终 FDC 结果也与 F1 分数一致。
英文摘要：
Flaky tests can pass or fail non-deterministically, without alterations to a software system. Such tests are frequently encountered by developers and hinder the credibility of test suites. State-of-the-art research incorporates machine learning solutions into flaky test detection and achieves reasonably good accuracy. Moreover, the majority of automated flaky test repair solutions are designed for specific types of flaky tests. This research work proposes a novel categorization framework, called FlaKat, which uses machine-learning classifiers for fast and accurate prediction of the category of a given flaky test that reflects its root cause. Sampling techniques are applied to address the imbalance between flaky test categories in the International Dataset of Flaky Test (IDoFT). A new evaluation metric, called Flakiness Detection Capacity (FDC), is proposed for measuring the accuracy of classifiers from the perspective of information theory and provides proof for its effectiveness. The final FDC results are also in agreement with F1 score regarding which classifier yields the best flakiness classification.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
