Fault Localization Based on Natural Language Processing

Yi Zeng

doi:10.54097/cpl.v11i2.12812

Authors

Yi Zeng

DOI:

https://doi.org/10.54097/cpl.v11i2.12812

Keywords:

Fault Localization; Natural Language Processing; Text Mining; Source Code Analysis.

Abstract

This paper presents a natural language processing-based defect localization methodology to identify and locate software defects in source code reviews. The methodology utilizes text mining and natural language processing techniques to extract defect-related information from software source code commit messages and bug reports. Through a series of experiments on a real-world dataset, we evaluate the effectiveness and efficiency of the proposed methodology in identifying and locating defects in software systems.

References

Yi Chen, et al. A survey of static code analysis techniques. IEEE Transactions on Software Engineering, 2018,44(5): p376-394.

Hui Liu, et al. Using natural language processing to improve software defect prediction. IEEE Transactions on Software Engineering, 2019, 46(3): p236-247.

Shao Y, Zhao J, Wang X, et al. Log V Research on Cross-Company Defect Prediction Method to Improve Software Security. IEEE International Conference on Software Quality, Reliability and Security (QRS), Lisbon, Portugal, 2018, p:111-122.

Volk M, Junges S, Katoen J P. Fast Dynamic Fault Tree Analysis by Model Checking Techniques. IEEE Transactions on Industrial Informatics, 2020, Vol. 14(18), p.370-379.

E. Leu, A. Schiper, A. Zramdini, Execution Replay on Distributed Memory Architectures. IEEE Proceedings Second Symposium on Parallel and Distributed Processing, 2017, p106-112.

Liu D.,Shen J.,Yang H. et al. Recognition and localization of actinidic arguta based on image recognition. Image Video Proc,2019, p19-201.

Zerdoumi S.， Sabri A.Q.M.， Kamsin A. et al. Image pattern recognition in big data: taxonomy and open challenges: survey . Multimed Tools Appl,2018, p10091-10121.

Kaur S.， Pandey S. Goel S. Plants Disease Identification and Classification Through Leaf Images: A Survey . Arch Computat Methods Eng,2019, p507-530.