Mitigating the Effect of Class Imbalance in Fault Localization Using Context-aware Generative Adversarial Network

Author:

Lei Yan1,Wen Tiantian1,Xie Huan1,Fu Lingfeng1,Liu Chunyan1,Xu Lei2,Sun Hongxia3

Affiliation:

1. Chongqing University,School of Big Data & Software Engineering,Chongqing,China

2. Haier Smart Home Co., Ltd.,Qingdao,China

3. Qingdao Haidacheng Purchasing Service Co., Ltd.,Qingdao,China

Funder

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

Publisher

IEEE

Reference83 articles.

1. A study of effectiveness of deep learning in locating real faults

2. Improving fault localization by integrating value and predicate based causal inference techniques;küçük;2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE),2021

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4. Effectiveness of Weighted Neural Network on Accuracy of Software Fault Localization

5. Automated Debugging Considered Harmful;xia;Considered Harmful A User Study Revisiting the Usefulness of Spectra-Based Fault Localization Techniques with Professionals Using Real Bugs from Large Systems " in Proceedings of the IEEE International Conference on Software Maintenance and Evolution (ICSME),2016

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3. On the Stability and Applicability of Deep Learning in Fault Localization;2024 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER);2024-03-12

4. A Data Augmentation Method for Fault Localization with Fault Propagation Context and VAE;IEICE Transactions on Information and Systems;2024-02-01

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