Registered user since Tue 6 Sep 2022
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Registered user since Tue 6 Sep 2022
Contributions
Late Breaking Results
Wed 12 Oct 2022 17:40 - 17:50 at Banquet B - Technical Session 17 - SE for AI Chair(s): Tim MenziesDespite the recent trend of developing and applying neural source code models to software engineering tasks, the quality of such models is insufficient for real-world use. This is because there could be noise in the source code corpora used to train such models. We adapt data-influence methods to detect such noises in this paper. Data-influence methods are used in machine learning to evaluate the similarity of a target sample to the correct samples in order to determine whether or not the target sample is noisy. Our evaluation results show that data-influence methods can identify noisy samples from neural code models in classification-based tasks. We anticipate that this approach will contribute to the larger vision of developing better neural source code models from a data-centric perspective, which is a key driver for developing useful source code models in practice.