JFrog announced a new machine learning (ML) lifecycle integration between JFrog Artifactory and MLflow, an open source software platform originally developed by Databricks.
The way that we develop and deliver software has changed dramatically in the past 5 years — but the metrics we use to measure quality remain largely the same. Despite seismic shifts in business expectations, development methodologies, system architectures, and team structures, most organizations still rely on quality metrics that were designed for a much different era.
Every other aspect of application delivery has been scrutinized and optimized as we transform our processes for DevOps. Why not put quality metrics under the microscope as well?
Are metrics like number of automated tests, test case coverage, and pass/fail rate important in the context of DevOps, where the goal is immediate insight into whether a given release candidate has an acceptable level of risk? What other metrics can help us ensure that the steady stream of updates don’t undermine the very user experience that we’re working so hard to enhance?
To provide the DevOps community an objective perspective on what quality metrics are most critical for DevOps success, Tricentis commissioned Forrester to research the topic. The results are published in a new ebook, Forrester Research on DevOps Quality Metrics that Matter: 75 Common Metrics—Ranked by Industry Experts.
The goal was to analyze how DevOps leaders measured and valued 75 quality metrics (selected by Forrester), then identify which metrics matter most for DevOps success. Here’s a look at the process:
1. Survey 603 global enterprise leaders responsible for their firms’ DevOps strategies.
2. From that sample, identify the firms with mature and successful DevOps adoptions (157 met Forrester’s criteria for this distinction).
3. Learn what quality metrics those experts actually measure, and how valuable they rate each metric that they regularly measure.
4. Use those findings to rate and rank each metric’s usage (how often experts use the metric) and value (how highly experts value the metric).
5. Compare the DevOps experts’ quality metric usage vs that of DevOps laggards. If there was a significant discrepancy, the metric is considered a DevOps differentiator.
The 75 DevOps quality metrics were divided into 4 categories:
■ Build
■ Functional validation
■ Integration testing
■ End-to-end regression testing
For each category of quality metrics, we came up with a heat map showing usage vs. value rankings. For example, here is the heat map for the Build category metrics.
We also plotted the data for each metric into a quadrant with 4 sections:
■ Value added: Metrics that are used frequently by DevOps experts and consistently rated as valuable by the organizations who measure them.
■ Hidden gem: Metrics that are not used frequently by DevOps experts, but are consistently rated as valuable by the organizations who measure them.
■ Overrated: Metrics that are used frequently by DevOps experts, but not rated as valuable by the organizations who measure them.
■ Distraction: Metrics that are not used frequently by DevOps experts, and not rated as valuable by the organizations who measure them.
For example, here is the quadrant for Build category metrics:
The ebook provides both heat maps and quadrants for all 4 categories, a quick look at each of the 75 metrics, per-category and overall analyses, and a few fun lists. Here’s a preview…
Hidden Gems
The following metrics are not commonly used (even among DevOps experts), but are ranked as extremely valuable by the select teams who actually measure them:
1. New defects (IT)
2. Critical defects (FV)
3. Automated tests prioritized by risk (Build)
4. Code coverage (Build)
5. Test cases executed (Build)
6. Static analysis results (Build)
7. Variance from baselines of percent of test cases passed (E2E)
8. Release readiness (E2E)
Top DevOps Differentiators
DevOps experts/leaders measure the following metrics significantly more than DevOps laggards measure them:
1. Automated tests prioritized by risk (Build)
2. Percent of automated E2E test cases (E2E)
3. Risk coverage (IT)
4. Release readiness (FV, IT, E2E)
5. Test efficiency (FV and IT)
6. Requirements covered by tests (Build, FV, IT, E2E)
7. Test case coverage (Build, E2E)
8. Static analysis results (Build)
9. Variance from baseline of percent of test cases passed (E2E)
10. Test effectiveness (FV, IT, E2E)
Most Used by DevOps Experts
The following metrics are the most frequently used (overall) by DevOps experts/leaders:
1. Test case coverage (E2E)
2. Pass/fail rate (FV)
3. API pass/fail rate (IT)
4. Number of tests executed (E2E)
5. API bug density (IT)
6. Requirements covered by tests (FV)
7. Requirements covered by tests (E2E)
8. Blocked test cases (FV)
9. Percent of automated E2E test cases (E2E)
10. Successful code builds (build)
Most Valued by DevOps Experts
The following metrics are the most highly-valued (overall) by DevOps experts/leaders:
1. Requirements covered by API tests (IT)
2. Percent of automated E2E tests (E2E)
3. Requirements covered by tests (E2E)
4. Requirements covered by tests (FV)
5. Count of critical functional defects (FV)
6. Total number of defects discovered in test (E2E)
7. Number of test cases executed (E2E)
8. Pass fail rate (FV)
9. New API defects found (IT)
10. Automated tests prioritized by risk (build)
Industry News
Copado announced the general availability of Test Copilot, the AI-powered test creation assistant.
SmartBear has added no-code test automation powered by GenAI to its Zephyr Scale, the solution that delivers scalable, performant test management inside Jira.
Opsera announced that two new patents have been issued for its Unified DevOps Platform, now totaling nine patents issued for the cloud-native DevOps Platform.
mabl announced the addition of mobile application testing to its platform.
Spectro Cloud announced the achievement of a new Amazon Web Services (AWS) Competency designation.
GitLab announced the general availability of GitLab Duo Chat.
SmartBear announced a new version of its API design and documentation tool, SwaggerHub, integrating Stoplight’s API open source tools.
Red Hat announced updates to Red Hat Trusted Software Supply Chain.
Tricentis announced the latest update to the company’s AI offerings with the launch of Tricentis Copilot, a suite of solutions leveraging generative AI to enhance productivity throughout the entire testing lifecycle.
CIQ launched fully supported, upstream stable kernels for Rocky Linux via the CIQ Enterprise Linux Platform, providing enhanced performance, hardware compatibility and security.
Redgate launched an enterprise version of its database monitoring tool, providing a range of new features to address the challenges of scale and complexity faced by larger organizations.
Snyk announced the expansion of its current partnership with Google Cloud to advance secure code generated by Google Cloud’s generative-AI-powered collaborator service, Gemini Code Assist.
Kong announced the commercial availability of Kong Konnect Dedicated Cloud Gateways on Amazon Web Services (AWS).
Pegasystems announced the general availability of Pega Infinity ’24.1™.