An unsupervised cross project model for crashing fault residence identification-Reference-Cited by-同舟云学术

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An unsupervised cross project model for crashing fault residence identification

Published:2022-09-29 Issue:6 Volume:16 Page:630-646
ISSN:1751-8806
Container-title:IET Software
language:en
Short-container-title:IET Software

Author:

Liu Xiao¹²,Xu Zhou¹²^ORCID,Yang Dan¹²,Yan Meng¹²^ORCID,Zhang Weihan²,Zhao Haohan²,Xue Lei³,Fan Ming¹⁴

Affiliation:

1. Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education Chongqing University Chongqing China

2. School of Big Data and Software Engineering Chongqing University Chongqing China

3. School of Cyber Science and Technology Sun Yat‐Sen University Shenzhen China

4. Ministry of Education Key Laboratory of Intelligent Networks and Network Security Xi'an Jiaotong University Xi'an Shaanxi China

Publisher

Institution of Engineering and Technology (IET)

Subject

Computer Graphics and Computer-Aided Design

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1049/sfw2.12073

Reference55 articles.

1. Shin Y. Williams L.:An empirical model to predict security vulnerabilities using code complexity metrics. In:Proceedings of the Second ACM‐IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM) pp.315–317(2008)

2. Does the fault reside in a stack trace? Assisting crash localization by predicting crashing fault residence

3. Identifying Crashing Fault Residence Based on Cross Project Model

4. Zhao K. et al.:Predicting Crash Fault Residence via Simplified Deep Forest Based on a Reduced Feature Set.arXiv preprint arXiv:210401768 (2021)

5. Cross-project defect prediction using a connectivity-based unsupervised classifier

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