Devops Analytics: Has Your Firm Got the Right Toolkit?

Author:Kinsbruner, Eran

We all know that DevOps under enormous pressure, thanks largely to the principles of Agile development. The intense focus on time to market can leave many development teams scrambling. If it's managed badly, a commitment to velocity of releases can mean that DevOps are thrown a never-ending release cycle, becoming so focused on the day to day that even major technological shifts can pass them by.

The only feasible way to iterate more quickly whilst ensuring quality is to implement Continuous Testing (CT) at the heart of the overall development strategy. This process of evaluating quality at every step of the Continuous Delivery Process means that teams can test early and test often. The benefits of CT are well established: it improves code quality, helps to assess exact business risk coverage, seamlessly integrates into DevOps process and, ultimately, helps to create an agile and reliable process in just hours instead of months.

However, the challenges of CT are significant - specifically the infinite amounts of data that is produced, all of which requires analysis. Indeed, research from Perfecto tells us that organisations spend between 50 - 72 hours per regression cycle analysing test results, filtering out noise and assessing failures which may impact their software releases. Without automated smart test report analysis, organisations spend precious time in manually drilling into long reports and developing in house reporting tools that are hard to maintain and use across teams.

So, as more teams integrate CT into their strategy, it is clear that analysing, understanding and filtering the data quickly is critical in order to prevent bottlenecking the DevOps process. The difficulty is that teams simply don't have the time.

How can teams overcome this? What new solutions are available to help them manage large test results datasets in a CI / DevOps environment without producing an enormous time-sink? The good news is that, from our own Test Reporting and Analytics Dashboard, to AI noise reduction, there are numerous tools that enable teams to quickly and efficiently analyse data, triage issues, and act upon failures with the best possible insights.

Here are the top five features to look for in a toolkit

  1. Executive dashboards Dashboards have evolved significantly over the years, --allowing developer and Quality Assurance (QA) managers to easily examine the pipeline, see CI trends related to time, build health and more. Simply put, a DevOps...

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