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article imageNew platform improves software development analytics

By Tim Sandle     Jun 25, 2018 in Business
London - The company Electric Cloud has released the platform ElectricFlow DevOps Foresight. The system applies machine learning algorithms to the data generated by tool chains to develop a risk score metric.
Electric Cloud released the ElectricFlow DevOps Foresight platform on June 25, 2018, at the DevOps Enterprise Summit London. The summit brings together technology and business leaders from around the world to examine the digital transformations necessary to drive large, complex organizations in today's digital world.
With the new platform, DevOps Foresight applies machine learning algorithms to the large quantities of data generated via tool chains (a set of distinct software development tools that are linked together by specific stages). These data are used by the platform to produce a risk score metric. This metric can predict the expected outcomes of releases before they move into production. Taking the predictive analytics concept further, DevOps Foresight also has the ability to demonstrate to software production managers where they need to improve pipelines. This is based on factors like developer influence and the code complexity.
A design aim of ElectricFlow DevOps Foresight is to provide development and operations groups with the key steps to eliminate the multiple sources of “release anxiety”. This is achieved by providing insight into software development practices and release schedules. This means that project pauses and operational inefficiencies throughout the software design-to-production process should become easier to identify and correct.
Furthermore, the platform has the ability to understand resource allocation for new applications. According to Carmine Napolitano, who is the CEO of Electric Cloud, in a communication sent to Digital Journal: "Improving the pipeline is often based on trial and error or best guesses. What we aim to do with ElectricFlow DevOps Foresight is provide data-driven insights much earlier in the process by looking at past successes, build complexity, author profiles and more, and then show where the pipeline can be improved based on facts."
More about ElectricFlow DevOps Foresight, Manufacturing, Analytics, Production
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