Abstract
Despite the recent trend of creating source code models and applying them to software engineering tasks, the quality of such models is insufficient for real-world application. In this work, we focus on improving existing code learning models from the data-centric perspective instead of designing new source code models. We shed some light on this direction by using a so-called data-influence method to identify noisy samples of pre-trained code learning models. The data-influence method is to assess the similarity of a target sample to the correct samples to determine whether or not such the target sample is noisy. The results of our evaluation show that data-influence methods can identify noisy samples for the code classification and defection prediction tasks. We envision that the data-centric approach will be a key driver for developing source code models that are useful in practice.
Abstract (translated)
URL
https://arxiv.org/abs/2205.13022
