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  • Big Data & Technology
  • Eran Kinsbruner
  • JUL 03, 2019

The Essential Toolbox for DevOps Analytics

Essential Toolbox for DevOps Analytics

With DevOps teams under constant pressure to improve software delivery cycles while maintaining—and improving—quality, many have turned to continuous testing (CT), implementing it as part of their overall development strategy. CT offers a number of benefits to DevOps teams, but it also introduces new challenges, specifically infinite amounts of data that require analysis. As more teams integrate CT into their strategy, it is clear that analyzing, understanding and filtering the results data quickly is critical to preventing bottlenecks in the DevOps process. The problem is, teams simply don’t have the time needed to analyze data.

Current research indicates that teams spend anywhere between 50 and 72 hours per regression cycle analyzing test results. This includes filtering out noise and assessing failures that may impact their software releases. However, the amount of time DevOps teams have to review and qualify test results has shrunk dramatically in recent years—from days to hours or even minutes, in some cases—leaving teams scrambling.

With pressure to accelerate velocity and deliver consistent, high-quality experiences, DevOps team require new analytics solutions to help them manage large test-results datasets in a CI/DevOps environment.

Let’s explore five essential tools that enable DevOps teams to quickly and efficiently analyze data, triage issues and act upon failures with the best possible insights.

Executive Dashboards

Dashboards have evolved immensely over the years, allowing dev managers and QA managers to easily examine the pipeline, see CI trends related to time, build health and more. When looking for the best solution, make sure it includes quality heat maps and CI dashboards, which make it easy to spot an anomaly in the CI pipeline and allow managers, DevOps teams and practitioners to quickly drill down into the single test report and the issue.

Single Test Report Visibility With Advanced Reporting Artifacts

With a single test report, practitioners have access to the entire flow of test steps, video, logs, screenshots and Jira–even the ability to drill down into the source code. This makes it quick and easy to locate an issue. For teams, these means more time fixing and less time searching.

Cross-Platform Reports

These analytics reports show the UI/UX simultaneously across multiple screen sizes, resolutions and platforms. Since the boundaries between the digital platforms need to be seamless, whether on desktop browsers or mobile devices, having the ability to see the UI/UX on multiple form factors (and layouts) at the same time is essential to assess overall quality.

DevOps Noise Reduction Tools Powered by AI and Analytics 

Today, finding which error classifications are true application bugs is critical to efficiency. Noise reduction tools that are powered by AI allow teams to filter out problems caused by various issues that are not software defects—for example, device connectivity issues and wrong scripting. Having a tool with the ability to locate each failed test execution, the root cause and the classification category is a huge productivity boost for both the test engineer and the developer.

DevOps and Actionable Insights

Analyzing data is one thing, knowing what to do it with it is another. Today, there are a number of analytics solutions on the market that provide teams with both capabilities. Once an issue is detected, classified and reported, having deep insight brings practitioners halfway toward the resolution of the issue.

To succeed in software delivery, teams not only need better automation as part of their processes, test flows and CI/CD workflows, but also an analytics platform to manage all of their test results data. Since DevOps involves a collection of team members from all parts of the software delivery lifecycle (SDLC) process, the central platform needs to meet the needs of all team members. As you work to build your next test analysis toolbox, consider the five features above to efficiently evaluate the data, act upon it and deliver iterations and features with confidence.

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