Show HN: Codemonkey.ai – Using ML/AI to improve the SDLC

Hey HN Community!

My team and I recently started Codemonkey.ai to solve many of the challenges we have faced over the years running large software organizations.

We found it harder and harder to optimize team productivity, measure software quality, and proactively manage risk as we began to support multiple applications built on different technologies/toolchains with development teams located across the globe. Codemonkey.ai was built to provide us with the insights we needed to be better software development leaders.

We are currently working with a number of software development organizations and are beginning to open it up to a wider audience. We would love to get your feedback and answer any questions you may have.

There is a 15-day trial (no CC Required) to give it a whirl! After you sign up you can even access a number of Open Source Projects to help you get a better idea of what Codemonkey.ai can do for your own apps.

Thanks, The Codemonkey.ai Team

Absolutely. Codemonkey.ai analyzes every source code commit and correlates each with an issue tracking system to understand when defects are introduced, features added, etc.

We build a predictive model using the prior commit history as a training set to predict the risk of new commits going forward. This model leverages over 50+ metrics that we derive from each commit.

With this model we can do the following: * Correlate code commits/changes to features, stories, and work items to understand the quality and risk impact that those things have on the codebase * Utilize clustering to provide insights around developer productivity and quality impact to the codebase * Utilize clustering to help with sizing of future features based on historical actuals

These are just a few of the areas where we are using ML within Codemonkey.ai today. In the future our goal is to tie additional information from production environments into this model as well.