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
기존 신경망 기반 결함 위치 파악은 명령문이 오류인지 여부에 대한 정보를 활용합니다. 처형 된 or 실행되지 않음 잠재적으로 실패의 원인이 되는 의심스러운 진술을 식별합니다. 그러나 정보는 명령문의 바이너리 실행 상태만 표시할 뿐 명령문이 실행에 얼마나 중요한지는 보여줄 수 없습니다. 결과적으로 결함 위치 파악 효율성이 저하될 수 있습니다. 이러한 문제를 해결하기 위해 본 논문에서는 용어 빈도-역 문서 빈도를 이용하여 실행 시 명령문의 영향력이 높은지 낮은지를 식별하는 TFIDF-FL을 제안합니다. 8개의 실제 프로그램에 대한 경험적 결과에 따르면 TFIDF-FL은 결함 위치 파악 효율성을 크게 향상시키는 것으로 나타났습니다.
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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부
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
부
@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},}
부
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 -