Learning to Find Naming Issues with Big Code and Small Supervision
Jingxuan He, Cheng-Chun Lee, Veselin Raychev, Martin Vechev. PLDI 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.
We implemented our method in a system called Namer and evaluated it on a large number of Python and Java programs. We demonstrate that Namer is effective in finding naming mistakes in real world repositories with high precision (∼70%). Perhaps surprisingly, we also show that existing deep learning methods are not practically effective and achieve low precision in finding naming issues (up to ∼16%).
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