TFIDF-FL: Localizing Faults Using Term Frequency-Inverse Document Frequency and Deep Learning
TFIDF-FL: Localizing Faults Using Term Frequency-Inverse Document Frequency and Deep Learning
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DOI：
10.1587/transinf.2018edl8237
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发表时间：
2019-09
期刊：
影响因子：
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通讯作者：
Zhuo Zhang;Yan Lei;Jianjun Xu;Xiaoguang Mao;Xi Chang
中科院分区：
文献类型：
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作者：
Zhuo Zhang;Yan Lei;Jianjun Xu;Xiaoguang Mao;Xi Chang
现有的基于神经网络的故障定位利用语句是否被执行的信息来识别可能导致故障的可疑语句。然而，这些信息只是显示了语句的二进制执行状态，而不能显示语句在执行中的重要性。因此，它可能会降低故障定位的有效性。为了解决这个问题，本文提出TFIDF-FL通过使用词频-逆文档频率来识别语句在执行中的影响程度的高或低。8个真实程序的实验结果表明，TFIDF-FL显著提高了故障定位的有效性。关键词：调试，故障定位，词频，逆文档频率，深度学习
Existing fault localization based on neural networks utilize the information of whether a statement is executed or not executed to identify suspicious statements potentially responsible for a failure. However, the information just shows the binary execution states of a statement, and cannot show how important a statement is in executions. Consequently, it may degrade fault localization effectiveness. To address this issue, this paper proposes TFIDF-FL by using term frequency-inverse document frequency to identify a high or low degree of the influence of a statement in an execution. Our empirical results on 8 real-world programs show that TFIDF-FL significantly improves fault localization effectiveness. key words: debugging, fault localization, term frequency, inverse document frequency, deep learning