The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Penyetempatan kerosakan sedia ada berdasarkan rangkaian saraf menggunakan maklumat sama ada sesuatu pernyataan itu dilaksanakan or tidak dilaksanakan untuk mengenal pasti kenyataan yang mencurigakan yang berpotensi bertanggungjawab untuk kegagalan. Walau bagaimanapun, maklumat tersebut hanya menunjukkan keadaan pelaksanaan binari bagi pernyataan, dan tidak dapat menunjukkan betapa pentingnya pernyataan dalam pelaksanaan. Akibatnya, ia boleh merendahkan keberkesanan penyetempatan kerosakan. Untuk menangani isu ini, kertas kerja ini mencadangkan TFIDF-FL dengan menggunakan frekuensi dokumen songsang istilah untuk mengenal pasti tahap tinggi atau rendah pengaruh sesuatu pernyataan dalam pelaksanaan. Keputusan empirikal kami pada 8 program dunia sebenar menunjukkan bahawa TFIDF-FL meningkatkan keberkesanan penyetempatan kerosakan dengan ketara.
Zhuo ZHANG
National University of Defense Technology
Yan LEI
Chongqing University
Jianjun XU
National University of Defense Technology
Xiaoguang MAO
National University of Defense Technology
Xi CHANG
National University of Defense Technology
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Salinan
Zhuo ZHANG, Yan LEI, Jianjun XU, Xiaoguang MAO, Xi CHANG, "TFIDF-FL: Localizing Faults Using Term Frequency-Inverse Document Frequency and Deep Learning" in IEICE TRANSACTIONS on Information,
vol. E102-D, no. 9, pp. 1860-1864, September 2019, doi: 10.1587/transinf.2018EDL8237.
Abstract: 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.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2018EDL8237/_p
Salinan
@ARTICLE{e102-d_9_1860,
author={Zhuo ZHANG, Yan LEI, Jianjun XU, Xiaoguang MAO, Xi CHANG, },
journal={IEICE TRANSACTIONS on Information},
title={TFIDF-FL: Localizing Faults Using Term Frequency-Inverse Document Frequency and Deep Learning},
year={2019},
volume={E102-D},
number={9},
pages={1860-1864},
abstract={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.},
keywords={},
doi={10.1587/transinf.2018EDL8237},
ISSN={1745-1361},
month={September},}
Salinan
TY - JOUR
TI - TFIDF-FL: Localizing Faults Using Term Frequency-Inverse Document Frequency and Deep Learning
T2 - IEICE TRANSACTIONS on Information
SP - 1860
EP - 1864
AU - Zhuo ZHANG
AU - Yan LEI
AU - Jianjun XU
AU - Xiaoguang MAO
AU - Xi CHANG
PY - 2019
DO - 10.1587/transinf.2018EDL8237
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E102-D
IS - 9
JA - IEICE TRANSACTIONS on Information
Y1 - September 2019
AB - 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.
ER -