DevOps Foresight: Beyond Prediction
The crystal ball has long been a staple of mythology – knowing what the future holds has a compelling allure. However, while the crystal ball could predict the future, that future was simply one of many possible outcomes. What usually is missing from these hero’s journeys is how the past (hindsight) informs us in the present (insight), how together they might be used to predict the future and, most importantly, what can be done to change the future for the better (foresight). The heroes are often left to figure this out on their own through trial and error, harrowing escapes, and help from unlikely sources.
The Hero’s (DevOps) Journey
We don’t want the same thing to happen with predictive analytics. It’s a great first step to be able to establish a risk score for a particular release, based on multiple factors like the author, code complexity, and environments. But, like heroes of old, how can we help DevOps Heroes determine how to change their future (delivery pipelines) for the better, while avoiding those harrowing escapes?
ElectricFlow DevOps Foresight
Today, Electric Cloud is announcing ElectricFlow DevOps Foresight: our predictive analytics solution that helps answer that question by extending beyond prediction into insightful recommendation. It works by performing machine learning on the mountains of data collected from key parts of the DevOps toolchain. Using self-adjusting algorithms, ElectricFlow DevOps Foresight deeply analyzes data already present in the entire DevOps toolchain, from ALM all the way to IT Service Management tools, to identify the patterns hidden in that data that predict the success or failure of builds, tests, deployments, or overall releases.
Patterns provide unprecedented insight into releases. They give hard evidence to the things that you may have suspected, like why the output of team X is always better than others, or it can even provide evidence to shatter other internal myths – like ‘your star developer can do no wrong.’
Pattern identification is just the beginning. ElectricFlow DevOps Foresight uses these patterns not only to predict a future risk score – exactly what you’d expect a “predictive analytics” solution to do – but it also provides the exact provenance for the score. This shows you exactly where changes may need to be made, like adding more stages to your pipeline to increase confidence, etc.
ElectricFlow DevOps Foresight uses your own data to provide actionable analytics that answer questions such as:
- “What changes should be done to a Release Pipeline to ensure higher quality by automatically detecting how complex the code changes are and how have changes to that file impacted outcomes in the past?”
- “My code is new but relatively simple, which route through the pipeline and what test suites would be best suited for it? Which tests can be skipped?”
- “What should I account for in the Release Pipeline depending on the authors assigned to the story and the code being changed?”
If you’d like to get a preview of the future of Continuous Delivery, join us on Monday and Tuesday, June 25 and 26, at DOES UK in London. Not in London this week? Sign up here for an upcoming webinar hosted by our own Mark Sigler and Ken McKnight to learn more.
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