CodART vs Kodebaze
CodART
github.com
Kodebaze
kodebaze.comSide by side
| Figure | CodART | Kodebaze |
|---|---|---|
| Price from | Free | — |
| Free plan | Yes | — |
| Free trial | — | — |
| Open source | — | — |
| Web | Yes | — |
| Linux | Yes | — |
| API | Yes | — |
| Self-hosted | Yes | Yes |
| Plans published | 1 | 0 |
| Spec score · Code Refactoring Tools | 8.0 (#8) | 7.3 (#17) |
| Deployment | The repository provides Docker Compose instructions for running its services locally. | Kodebaze supports on-premises, private cloud, and country-restricted or region-locked deployments without requiring public SaaS. |
| Purpose | CodART is a toolkit for automated refactoring and multi-objective optimization of Java source code. | Kodebaze is an AI-powered refactoring platform that helps enterprises understand, refactor, and modernize large legacy codebases. |
| Support | The repository invites questions by email at [email protected] and welcomes project contributions and discussion comments. | Every deployment includes a Forward Deployed Engineer who deploys and configures the platform, supports initial workflows, and transfers knowledge to the customer’s team. |
Plans
In their own words
CodART
CodART is a toolkit for automated refactoring and multi-objective optimization of Java source code. It documents 47 refactoring operations, including Extract Class, Move Method, and Encapsulate Field, and supports project-level and batch work. Its optimization uses NSGA-II and NSGA-III across eight QMOOD quality metrics. The toolkit also includes PPO-based generation of refactoring sequences and models that predict testability from source code metrics. PMD integration with custom rulesets supports code smell detection, while SciTools Understand handles code parsing and metric computation. The documented interfaces are a React web interface and a FastAPI REST API, and Docker Compose instructions explain how to run the services locally. CodART is free under an MIT License, which permits use, copying, modification, distribution, sublicensing, and sale subject to its conditions. The setup requires a separate SciTools Understand professional license; academic licenses are available for research purposes. The quick-start calls for at least 8 GB RAM and 4 CPU cores, with 12 GB or more recommended for large projects. Java is its listed supported language.