测试策略对集成学习缺陷预测在线优化影响的实证研究
An Empirical Study of the Impact of Test Strategies on Online Optimization for Ensemble-Learning Defect Prediction
AI总结：
针对跨项目集成缺陷预测方法选择不稳定的问题，本文用BA在线选择集成方法，并实证测试顺序影响，发现PF策略可提升准确率并多发现7%缺陷。
摘要：
集成学习方法已被用于提升缺陷预测模型的可靠性。然而，在不同软件项目中，单一方法能否获得最高准确率，其稳定性尚无定论。本工作旨在通过帮助选择准确率最高的集成方法，提升此类项目中集成学习缺陷预测的性能。我们采用 bandit algorithms（BA，赌博机算法）这一在线优化方法来选择准确率最高的集成方法。每个软件模块按顺序接受测试，BA利用这些模块的测试结果来评估各集成学习方法的性能。在应用在线优化时，所遵循的测试策略可能影响测试工作量和预测准确率。因此，我们分析了测试顺序对BA性能的影响。实验中，我们使用了6个常用缺陷预测数据集、4种集成学习方法（如 bagging）以及3种测试策略（如优先测试预测为正的模块，PF）。结果表明，当BA与PF结合使用时，预测准确率平均有所提升，并且在6个数据集中至少5个上发现的缺陷数量增加了7%（不过相较普通集成学习，测试工作量略有约4%的增加）。因此，采用PF策略的BA最能有效利用集成方法在不同项目上获得最高预测准确率。
英文摘要：
Ensemble learning methods have been used to enhance the reliability of defect prediction models. However, there is an inconclusive stability of a single method attaining the highest accuracy among various software projects. This work aims to improve the performance of ensemble-learning defect prediction among such projects by helping select the highest accuracy ensemble methods. We employ bandit algorithms (BA), an online optimization method, to select the highest-accuracy ensemble method. Each software module is tested sequentially, and bandit algorithms utilize the test outcomes of the modules to evaluate the performance of the ensemble learning methods. The test strategy followed might impact the testing effort and prediction accuracy when applying online optimization. Hence, we analyzed the test order's influence on BA's performance. In our experiment, we used six popular defect prediction datasets, four ensemble learning methods such as bagging, and three test strategies such as testing positive-prediction modules first (PF). Our results show that when BA is applied with PF, the prediction accuracy improved on average, and the number of found defects increased by 7% on a minimum of five out of six datasets (although with a slight increase in the testing effort by about 4% from ordinal ensemble learning). Hence, BA with PF strategy is the most effective to attain the highest prediction accuracy using ensemble methods on various projects.
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。
