Software aging refers to a problem of performance decay in the software systems, which are running for a long period. The primary cause of this phenomenon is the accumulation of run-time errors in the software, which are also known as aging-related bugs (ARBs). Many efforts have been reported earlier to predict the origin of ARBs in the software so that these bugs can be identified and fixed during testing. Imbalanced dataset, where the representation of ARBs patterns is very less as compared to the representation of the non-ARBs pattern significantly hinders the performance of the ARBs prediction models. Therefore, in this article, we present an oversampling approach, generative adversarial networks-based synthetic data generation-based ARBs prediction models. The approach uses generative adversarial networks to generate synthetic samples for the ARBs patterns in the given datasets implicitly and build the prediction models on the processed datasets. To validate the performance of the presented approach, we perform an experimental study for the seven ARBs datasets collected from the public repository and use various performance measures to evaluate the results. The experimental results showed that the presented approach led to the improved performance of prediction models for the ARBs prediction as compared to the other state-of-the-art models.

中文翻译：

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基于生成对抗网络的软件老化相关错误预测中的不平衡学习

软件老化是指长时间运行的软件系统出现性能衰减的问题。这种现象的主要原因是软件中运行时错误的累积，这些错误也称为与老化相关的错误 (ARB)。之前已经报道了许多努力来预测软件中 ARB 的来源，以便在测试期间识别和修复这些错误。不平衡数据集，其中 ARB 模式的表示与非 ARB 模式的表示相比非常少，这显着阻碍了 ARB 预测模型的性能。因此，在本文中，我们提出了一种过采样方法，即基于生成对抗网络的基于合成数据生成的 ARB 预测模型。该方法使用生成对抗网络为给定数据集中的 ARB 模式隐式生成合成样本，并在处理后的数据集上构建预测模型。为了验证所提出方法的性能，我们对从公共存储库收集的七个 ARB 数据集进行了实验研究，并使用各种性能指标来评估结果。实验结果表明，与其他最先进的模型相比，所提出的方法提高了 ARB 预测的预测模型的性能。我们对从公共存储库收集的七个 ARB 数据集进行了实验研究，并使用各种性能指标来评估结果。实验结果表明，与其他最先进的模型相比，所提出的方法提高了 ARB 预测的预测模型的性能。我们对从公共存储库收集的七个 ARB 数据集进行了实验研究，并使用各种性能指标来评估结果。实验结果表明，与其他最先进的模型相比，所提出的方法提高了 ARB 预测的预测模型的性能。