Smelling Source Code Using Deep Learning

Poor quality code contributes to increasing technical debt and makes the software difficult to extend and maintain. Code smells capture such poor code quality practices. Traditionally, the software engineering community identifies code smells in deterministic ways by using metrics and pre-defined rules/heuristics. Creating a deterministic tool for a specific language is an expensive and arduous task since it requires source code analysis starting from parsing, symbol resolution, intermediate model preparation, and applying rules/heuristics/metrics on the model. It would be great if we can leverage the tools available for one programming language and cross-apply them on another language. In this presentation, I would like to present our work on detecting smells using deep learning models.