TFIDF-FL: Localizing Faults Using Term Frequency-Inverse Document Frequency and Deep Learning

Zhuo ZHANG  Yan LEI  Jianjun XU  Xiaoguang MAO  Xi CHANG  

IEICE TRANSACTIONS on Information and Systems   Vol.E102-D   No.9   pp.1860-1864
Publication Date: 2019/09/01
Publicized: 2019/05/27
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2018EDL8237
Type of Manuscript: LETTER
Category: Software Engineering
debugging,  fault localization,  term frequency,  inverse document frequency,  deep learning,  

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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.