In this article, we propose a hierarchical fault localization technique using a deep neural network (DNN). First, we prioritize the functions of a program based on their suspiciousness score. Subsequently, the fault is localized to specific statements within the top k suspected functions, where the value of k is determined heuristically. We use two function-level features to train a DNN for fault localization at the function level. Subsequently, the invocation information of the statements of the top-k functions is used to train another neural network to localize the faulty statement. We also report an extension to our approach for localizing multiple faults. This involves partitioning the failed test cases into clusters such that they target different faults. Our empirical evaluation indicates that our proposed approach requires examining 30.05 to 50.74% less code on an average, as compared to related fault localization techniques.

中文翻译：

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使用 DNN 分层定位软件故障

在本文中，我们提出了一种使用深度神经网络 (DNN) 的分层故障定位技术。首先，我们根据程序的可疑性评分对程序的功能进行优先级排序。随后，故障被定位到前 k 个可疑函数中的特定语句，其中 k 的值是启发式确定的。我们使用两个函数级特征来训练 DNN 以在函数级进行故障定位。随后，利用top-k函数语句的调用信息训练另一个神经网络来定位错误语句。我们还报告了我们用于定位多个故障的方法的扩展。这涉及将失败的测试用例划分为集群，以便它们针对不同的故障。我们的实证评估表明，我们提出的方法需要检查 30。