Codeball

3.50

RATING

EXPOSURE

About this tool

Name

Codeball

Category

Coding

Codeball can help you ship code more confidently and promptly by automatically evaluating code with AI. The Codeball AI is trained on millions of code contributions to identify risky code updates. You can use GitHub: Actions to setup and run Codeball. To get started, add ".github/workflows/codeball.yml" to a repository. Codeball will analyze the pull request and add hundreds of criteria into the Codeball AI when analyzing fresh pull requests. Codeball can help you weigh the risks of your options before making a decision. By taking into account fresh issues and dangers, you can make a more informed decision that will help your project avoid potential problems down the line. There are more than 20 different programming languages out there, each with its own strengths and weaknesses. It can be tough to keep track of them all, let alone make decisions about which one to use for a particular project. Codeball can help take some of the guesswork out of the equation by automatically flagging potentially risky pull requests. This way, you can focus on shipping code quickly and with fewer errors. Codeball is the perfect place to get your PRs approved without needing a code review! Just configure Codeball to accept your PRs and you're all set. With Codeball, you can have faith in the completion of your tasks and prioritize getting them done quickly and easily.

How to use

There are a few steps you'll need to follow in order to get Codeball up and running using GitHub. You'll first need to initialize the repository by adding ".github/workflows/codeball.yml". From there, Codeball will evaluate new pull requests by adding hundreds of additional criteria to the Codeball AI. Next, you'll need to identify your risk. If there are any fresh issues or risks, Codeball will let you know. You can add tags, get rid of tests, or add advice as needed. Codeball can help you prioritize which pull requests to merge by identifying and flagging the risky ones. The artificial intelligence model is trained on millions of code contributions and can give each PR a risk score. This way, you can deliver more quickly and with fewer defects.

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