Learning to find naming issues with big code and small supervision
Jingxuan He, Cheng-Chun Lee, Veselin Raychev, Martin Vechev · 2021
We introduce a new approach for finding and fixing naming issues in source code. The method is based on a careful combination of unsupervised and supervised procedures: (i) unsupervised mining of patterns from Big Code that express common naming idioms. Program fragments violating such idioms indicates likely naming issues, and (ii) supervised learning of a classifier on a small labeled dataset which filters potential false positives from the violations.