Codeball approves Pull Requests that a human would have approved. This means less waiting for review. Less rubber stamping also means the team gets to spend more time on the trickier PRs.
We've been trying out Codeball for a few weeks now at Kitemaker and I have to say so far it's been remarkably accurate. We haven't seen a single false positive where it flags a PR as approved where we wouldn't have approved it ourselves manually.
Thanks @rememberlenny and for sure, happy to share!
Codeball is a Multi-layer Perceptron classifier neural network. It evaluates on a per PR file basis, taking in a very large number of inputs about the code and the author. Broadly speaking, they fall in three different categories โ Basic, Derived and Categorical.
- Basic โ Metadata directly associated with the Pull Request. For example, number of lines added, removed etc.
- Derived โ Data that is indirectly associated with the Pull request. Examples include number of days since last change to file, file ownership ratios, file volatility score, author-file score etc.
- Categorical variables โ Derived categories for the Pull Request, for instance the file type categories. A more advanced example category is the perceptual hash of file diffs.
We used millions of pull requests from open source projects in order to generate training data - the predictors as well as the known outcomes (whether the PR was merged without changes/objections). Having seen tons of success and failure patterns makes Codeball really precise in evaluating contributions it has never seen before.
Right now Codeball can only classify (and trigger approval of) safe PRs. We want to take it further and give the developer feedback on how to improve their code. Here is a comparison with the automation level for autonomous cars ;) ->
https://codeball.ai/what
This is so awesome and makes so much sense! Indeed there's a whole category of PRs that are trivial but you also don't want to break the good habit and so teams are ending up wasting lots of time
Thanks @igorzij, and I couldn't agree more. Bringing more automation further up the toolchain is a super interesting area, products that are "shifting left" are and will continue to be on the forefront of the industry!
Hey Product Hunters! ๐
As engineers, we hate being blocked and waiting for Pull Request reviews, especially when the changes are obviously safe. We thought โ if an AI can drive, surely we can train one to review code!
๐ง Codeball is a neural network trained on millions of Pull Requests (โwow, big data!โ). It accurately predicts PRs that would be approved without further feedback.
๐งฉ Use the Codeball GitHub Action and auto-approve your good PRs (https://github.com/sturdy-dev/co...)!
๐ฏ Optimized for precision โ Codeball only approves PRs that itโs really confident in. It uses over 100 different predictors (e.g. the code diffs themselves, author experience with the changed files as well as past issues with the type of change) for each contribution it checks.
๐คฏ When you stop the rubber stamping fakearoo on most PRs, you start actually paying more attention to tricky ones. This avoids the phenomenon informally known as โomg we have a bug in production!โ.
๐ฎ Play with the live demo at https://codeball.ai to see how Codeball would have performed on your repository and how much time (and money) you would have saved last month. If you like it, then you shoulda put the GitHub Action on it.
๐ธ Codeball is free for open source projects and costs $10 / user / month for private repositories. It has a trial, of course, and because we are ballers, Codeball only costs if it actually saves you โฐ & ๐ต (there's a pretty dashboard)!
Thanks!
@kenny_strubel Codeball is currently in beta, and is only available for GitHub at the moment. But we have both DevOps and other platforms on the roadmap coming soon. Please reach out to us in DM if you're interested in joining the invite-only beta for DevOps. :-)
Report
@zegl That sounds great! Where can I shoot you a dm?
Scrimba
Polar
GitButler
Public Art
GitButler
Digger.dev
Polar
GitButler
Polar
Polar