Neural language models for code quality identification
Srinivasan H. Sengamedu, Hangqi Zhao · 2022
Neural Language Models for code have lead to interesting applications such as code completion and bug fix generation. Another type of code related application is the identification of code quality issues such as repetitive code and unnatural code. Neural language models contain implicit knowledge about such aspects. We propose a framework to detect code quality issues using neural language models. To handle repository-specific conventions, we use local or repository-specific models. The models are successful in detecting real-world code quality issues with low false positive rate.