
Show HN: Codemonkey.ai – Using ML/AI to improve the SDLC - kylef14
https://www.codemonkey.ai
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kylef14
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

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tw582
Can you expand on how you are applying machine learning and the types of
insights you can provide?

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kylef14
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.

