Various software fault prediction models and techniques for building algorithms have been proposed. Many studies have compared and evaluated them to identify the most effective ones. However, in most cases, such models and techniques do not have the best performance on every dataset. This is because there is diversity of software development datasets, and therefore, there is a risk that the selected model or technique shows bad performance on a certain dataset. To avoid selecting a low accuracy model, we apply bandit algorithms to predict faults. Consider a case where player has 100 coins to bet on several slot machines. Ordinary usage of software fault prediction is analogous to the player betting all 100 coins in one slot machine. In contrast, bandit algorithms bet one coin on each machine (i.e., use prediction models) step-by-step to seek the best machine. In the experiment, we developed an artificial dataset that includes 100 modules, 15 of which include faults. Then, we developed various artificial fault prediction models and selected them dynamically using bandit algorithms. The Thomson sampling algorithm showed the best or second-best prediction performance compared with using only one prediction model.

中文翻译：

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针对软件故障预测外部有效性的 Bandit 算法仿真研究

已经提出了用于构建算法的各种软件故障预测模型和技术。许多研究对它们进行了比较和评估，以确定最有效的方法。但是，在大多数情况下，此类模型和技术并非在每个数据集上都具有最佳性能。这是因为软件开发数据集存在多样性，因此存在所选模型或技术在某个数据集上表现不佳的风险。为了避免选择低精度模型，我们应用了强盗算法来预测故障。考虑一个玩家有 100 个硬币可以在几台老虎机上下注的情况。软件故障预测的普通用法类似于玩家在一台老虎机中下注所有 100 个硬币。相比之下，强盗算法在每台机器上下注一枚硬币（即，使用预测模型）逐步寻找最佳机器。在实验中，我们开发了一个包含 100 个模块的人工数据集，其中 15 个包含故障。然后，我们开发了各种人工故障预测模型，并使用老虎机算法动态选择它们。与仅使用一种预测模型相比，Thomson 采样算法显示出最佳或次佳的预测性能。