Software aging is a phenomenon in which long-running software systems show an increasing failure rate and/or progressive performance degradation. Due to their nature, Aging-Related Bugs (ARBs) are hard to discover during software testing and are also challenging to reproduce. Therefore, automatically predicting ARBs before software release can help developers reduce ARB impact or avoid ARBs. Many bug prediction approaches have been proposed, and most of them show effectiveness in within-project prediction settings. However, due to the low presence and reproducing difficulty of ARBs, it is usually hard to collect sufficient training data to build an accurate prediction model. A recent work proposed a method named Transfer Learning based Aging-related bug Prediction (TLAP) for performing cross-project ARB prediction. Although this method considerably improves cross-project ARB prediction performance, it has been observed that its prediction result is affected by several key factors, such as the normalization methods, kernel functions, and machine learning classifiers. Therefore, this paper presents the first empirical study to examine the impact of these factors on the effectiveness of cross-project ARB prediction in terms of single-factor pattern, bigram pattern, and triplet pattern and validates the results with the Scott-Knott test technique. We find that kernel functions and classifiers are key factors affecting the effectiveness of cross-project ARB prediction, while normalization methods do not show statistical influence. In addition, the order of values in three single-factor patterns is maintained in three bigram patterns and one triplet pattern to a large extent. Similarly, the order of values in the three bigram patterns is also maintained in the triplet pattern.

中文翻译：

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TLAP跨项目老化相关bug预测影响因素实证研究

软件老化是一种长期运行的软件系统出现故障率增加和/或性能逐渐下降的现象。由于其性质，与老化相关的错误 (ARB) 在软件测试期间很难被发现，并且很难重现。因此，在软件发布前自动预测 ARB 可以帮助开发人员减少 ARB 影响或避免 ARB。已经提出了许多错误预测方法，其中大多数在项目内预测设置中显示出有效性。然而，由于 ARB 的低存在性和再现难度，通常很难收集足够的训练数据来构建准确的预测模型。最近的一项工作提出了一种名为 Transfer Learning based Aging-related bug Prediction (TLAP) 的方法，用于执行跨项目 ARB 预测。尽管该方法显着提高了跨项目 ARB 预测性能，但已经观察到其预测结果受到几个关键因素的影响，例如归一化方法、核函数和机器学习分类器。因此，本文提出了第一项实证研究，从单因素模式、二元模式和三元模式方面检验这些因素对跨项目 ARB 预测有效性的影响，并使用 Scott-Knott 检验技术验证结果. 我们发现核函数和分类器是影响跨项目 ARB 预测有效性的关键因素，而归一化方法没有表现出统计影响。此外，三个单因子模式中的值顺序在很大程度上保持在三个双元组模式和一个三元组模式中。同样，三个二元组模式中的值顺序也保持在三元组模式中。