HIGHLIGHTS
- who: ZHENGMAO LUO et al. from the College of Security Technology, Wenzhou, China , Faculty of Innovation Engineering, Macau University of Science and Technology, Macau, China have published the Article: Improving Bug Localization With Effective Contrastive Learning Representation, in the Journal: (JOURNAL)
- what: To resolve the above problem in this paper the authors propose a bug localization approach that combines pre-trained language models and contrastive learning namely CoLoc. The authors design a new contrastive learning objective to pretrain CoLoc, making it learn the semantic difference between unpaired bug reports and buggy files.
SUMMARY . . .
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