Electric Cloud develops 'credit score' for application success

There’s applications and application delivery… and then there’s the arguably more upmarket notion of Adaptive Release Orchestration & Continuous Delivery (AROCD – not a real acronym).

Placing itself unabashedly in the latter category is Electric Cloud.

The firm this month brings forth its ElectricFlow DevOps Foresight product as a piece of software designed to apply machine learning algorithms to the massive amounts of data generated by tool chains.

The software is capable of producing a ‘risk score metric’ that predicts the outcomes of releases before they head into production.

Taking predictive analytics even further, it will also illustrate where to improve pipelines based on metrics related to ‘developer influence’ (based on past performance and behaviour, presumably) and ‘code complexity’.

ElectricFlow DevOps Foresight is supposed to reduce bottlenecks and inefficiencies as it provides the ability to understand resource allocation for new and complex application and environment requirements.

A ‘credit score’ for apps

Much like a credit score, the creation of a release’s risk score numerical value is based on developer, code and environment profiles and gives everyone a visual way to interpret the likelihood of success for a particular build or pipeline.

If the score is high, DevOps teams can look at those profiles to determine what, specifically within those profiles, is driving up the risk.

Electric Cloud says that in order to illustrate areas for improving the pipeline, DevOps Foresight looks at contributing factors and what has helped to improve them in the past and will suggest appropriate changes in teams, code or environments.

CEO of Electric Cloud Carmine Napolitano has said that 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 then show where the pipeline can be improved based on facts,” said Napolitano.

Managers will be able to proactively answer questions such as:

  • Are we going to finish our release on time?
  • Can we move faster or can we do more?
  • Will this release cause more or less quality issues?
  • What’s the likelihood of a production deployment failure?

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