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
스펙트럼 기반 결함 위치 파악(SFL)는 오류가 있는 각 프로그램 구성 요소에 대한 의심을 측정하여 디버거가 오류의 근본 원인을 식별하고 가상의 오류 순위 목록을 생성하는 데 도움을 주는 경량 접근 방식입니다. 하지만 SFL 기술이 효과적인 것으로 나타났지만, 버그가 있는 프로그램의 결함 구성 요소는 복잡한 결함 유발 모델로 인해 항상 상위에 순위를 매길 수는 없습니다. 그러나 모든 버그가 있는 프로그램에 대해 복잡한 트리거링 모델을 모델링하는 것은 극히 어렵습니다. 이 문제를 해결하기 위해 우리는 두 가지 간단한 오류 트리거 모델을 제안합니다(RIPRα and RIPRβ) 및 결함 유발 모델에 따라 일부 상위 순위 구성 요소를 배제함으로써 두 가지 결함 유발 모델을 기반으로 결함 절대 순위를 향상시키는 정제 기술이 있습니다. 직관적으로, 결함 구성 요소가 다음 범위 내에서 순위가 매겨진 경우 우리의 접근 방식은 효과적입니다. 탑케이 두 가지 결함 위치 파악 전략에 의해 출력된 두 가지 결함 순위 목록에서. 실험 결과는 우리의 접근 방식이 세 가지 경우에서 결함 절대 순위를 크게 향상시킬 수 있음을 보여줍니다.
Yong WANG
Anhui Polytechnic University,Nanjing University of Aeronautics and Astronautics
Zhiqiu HUANG
Nanjing University of Aeronautics and Astronautics
Rongcun WANG
China University of Mining and Technology
Qiao YU
China University of Mining and Technology
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
부
Yong WANG, Zhiqiu HUANG, Rongcun WANG, Qiao YU, "Spectrum-Based Fault Localization Using Fault Triggering Model to Refine Fault Ranking List" in IEICE TRANSACTIONS on Information,
vol. E101-D, no. 10, pp. 2436-2446, October 2018, doi: 10.1587/transinf.2017EDP7386.
Abstract: Spectrum-based fault localization (SFL) is a lightweight approach, which aims at helping debuggers to identity root causes of failures by measuring suspiciousness for each program component being a fault, and generate a hypothetical fault ranking list. Although SFL techniques have been shown to be effective, the fault component in a buggy program cannot always be ranked at the top due to its complex fault triggering models. However, it is extremely difficult to model the complex triggering models for all buggy programs. To solve this issue, we propose two simple fault triggering models (RIPRα and RIPRβ), and a refinement technique to improve fault absolute ranking based on the two fault triggering models, through ruling out some higher ranked components according to its fault triggering model. Intuitively, our approach is effective if a fault component was ranked within top k in the two fault ranking lists outputted by the two fault localization strategies. Experimental results show that our approach can significantly improve the fault absolute ranking in the three cases.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2017EDP7386/_p
부
@ARTICLE{e101-d_10_2436,
author={Yong WANG, Zhiqiu HUANG, Rongcun WANG, Qiao YU, },
journal={IEICE TRANSACTIONS on Information},
title={Spectrum-Based Fault Localization Using Fault Triggering Model to Refine Fault Ranking List},
year={2018},
volume={E101-D},
number={10},
pages={2436-2446},
abstract={Spectrum-based fault localization (SFL) is a lightweight approach, which aims at helping debuggers to identity root causes of failures by measuring suspiciousness for each program component being a fault, and generate a hypothetical fault ranking list. Although SFL techniques have been shown to be effective, the fault component in a buggy program cannot always be ranked at the top due to its complex fault triggering models. However, it is extremely difficult to model the complex triggering models for all buggy programs. To solve this issue, we propose two simple fault triggering models (RIPRα and RIPRβ), and a refinement technique to improve fault absolute ranking based on the two fault triggering models, through ruling out some higher ranked components according to its fault triggering model. Intuitively, our approach is effective if a fault component was ranked within top k in the two fault ranking lists outputted by the two fault localization strategies. Experimental results show that our approach can significantly improve the fault absolute ranking in the three cases.},
keywords={},
doi={10.1587/transinf.2017EDP7386},
ISSN={1745-1361},
month={October},}
부
TY - JOUR
TI - Spectrum-Based Fault Localization Using Fault Triggering Model to Refine Fault Ranking List
T2 - IEICE TRANSACTIONS on Information
SP - 2436
EP - 2446
AU - Yong WANG
AU - Zhiqiu HUANG
AU - Rongcun WANG
AU - Qiao YU
PY - 2018
DO - 10.1587/transinf.2017EDP7386
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E101-D
IS - 10
JA - IEICE TRANSACTIONS on Information
Y1 - October 2018
AB - Spectrum-based fault localization (SFL) is a lightweight approach, which aims at helping debuggers to identity root causes of failures by measuring suspiciousness for each program component being a fault, and generate a hypothetical fault ranking list. Although SFL techniques have been shown to be effective, the fault component in a buggy program cannot always be ranked at the top due to its complex fault triggering models. However, it is extremely difficult to model the complex triggering models for all buggy programs. To solve this issue, we propose two simple fault triggering models (RIPRα and RIPRβ), and a refinement technique to improve fault absolute ranking based on the two fault triggering models, through ruling out some higher ranked components according to its fault triggering model. Intuitively, our approach is effective if a fault component was ranked within top k in the two fault ranking lists outputted by the two fault localization strategies. Experimental results show that our approach can significantly improve the fault absolute ranking in the three cases.
ER -