Electric Cloud, one of the leading in Continuous Delivery and Adaptive Release Orchestration have publicized "ElectricFlow DevOps Foresight" the new predictive analytics solution. ElectricFlow DevOps Foresight makes use of deep learning to recognize and establish release patterns in pipelines and gauge the probability of software release success and thereby provide recommendations to enhance pipelines.
ElectricFlow DevOps Foresight aims at applying machine language to gargantuan amounts of data that are generated by myriad tools. It then prepares a risk score metric and that helps in the prediction of outcomes of releases prior to production. It will take predictive analytics to yet another level giving recommendations to improve pipelines based on code complexity influence, developer influence and a lot more.
DevOps Foresight gives the DevOps Teams the provision to adopt necessary steps to erase the sources of release anxiety by giving access to a crystal clear insight into the existing practices of the development cycle and the expected release patterns. It is highly convenient to locate the bottlenecks in the existing software delivery process and thereby much easier to rectify the same. It gives the provision to the teams to establish the need or urgency for resource allocation for any new or relatively complex application and environment requirements. This carves the optimum roadway towards a successful software release.
Pattern Identification: DevOps Foresight helps in performing deep analytics on toolchain and the historical data identifies the patterns in the current release or the ongoing project and also over the time period.
Risk Scores by Release: It makes use of the patterns uncovered by the author or the code base and determine the risk score.
Risk Origin: It helps in drawing the complete details of the code complexity and the development team influence on the overall risk score.
Depending on the deep insights drawn from the past patterns of achieving success and failures DevOps Foresight predicts the possibility of a release's success. The fabrication of a release's risk score numerical value depends on the code complexity, developer, and environment profiles. It enables the stakeholders a visual way to comprehend the chances of success for a definite pipeline or build. If the score is high indicating a higher risk the DevOps teams can immediately cater to it and determine which profiles are driving up the risk.
If the team will complete release on time?
If more can be achieved or at a faster rate?
Whether the release will lead to fewer or more complex quality issues?
What are the chances of deployment failure?
Capability to apply machine learning algos to massive volumes of data is one of the major reasons to shift the DevOps processes into the cloud. DevOps Foresight allows the DevOps teams to leverage a turnkey application where support for such algorithms is embedded into the cloud service already. However, the question that lingers around is to what extent an organization is willing to rely on machine learning algos or AI to automate the DevOps processes.
Wrapping up it is needless to say that the customers need proper insights into releases moving through the pipeline, but this has consistently been a challenge to evaluate the risks of releases prior to getting started. Improving the pipeline mostly depends on guesses and trials but ElectricFlow Devops Foresight aims at providing data-driven insights at a pretty early stage by scrutinizing factors like previous successes, profiles, code complexity, etc and show the areas of improvement which are strictly fact-based. It is a vital requirement which aids in saving hundreds of hours of work and at the same time freeing them of unnecessary anxiety that comes along with every product they deliver.