To understand which quality metrics are actually most valuable in a DevOps context, Tricentis commissioned Forrester Consulting to survey DevOps practitioners worldwide. The resulting 55-page report—DevOps Quality Metrics that Matter: 75 Common Metrics—Ranked by Industry Experts—provides rankings, heat maps, quadrant mappings, and regional analysis. The findings below are drawn from that research.

Why Classic Quality Metrics Fall Short in DevOps

In traditional software delivery, quality gates were designed around infrequent, large releases. A test pass rate of 95% or a count of automated test cases made sense when releases happened quarterly. In a CI/CD pipeline, where small increments ship continuously, the same metrics fail to answer the question that actually matters: does this release candidate carry an acceptable level of risk for production?

Every other component of the DevOps toolchain—pipelines, infrastructure-as-code, observability—has been redesigned around speed and continuous feedback. Quality measurement has largely been left behind.

Global Findings: Three Key Takeaways

Across all regions surveyed, DevOps experts consistently ranked risk- and coverage-oriented metrics above raw counting metrics. Three patterns emerged clearly:

• Business risk framing drives metric selection. Organizations that reframed quality in terms of business risk shifted their measurement practices accordingly—tracking metrics that quantify risk exposure rather than test volume.
• Experts favour contextual metrics; laggards favour counting metrics. DevOps leaders gravitated toward metrics such as requirements coverage and risk coverage. Less mature teams continued to rely on metrics like number of tests executed—numbers that are easy to collect but difficult to act on.
• End-to-end user experience metrics separate leaders from laggards. High-maturity teams measured quality across full user journeys and end-to-end transactions. Lower-maturity teams measured quality at the application or team boundary, creating blind spots at integration points.

The overarching conclusion: DevOps quality transformation is not primarily a tooling problem. It requires a cultural and organisational shift toward shared ownership of business risk across development, testing, and operations.

Europe: Higher Measurement Commitment and Stronger Risk Focus

European respondents stood out for the breadth and depth of their quality metric measurement practices. Three regional trends were notable:

1. Broader Adoption of Quality Metrics Across the Board

For almost all metrics surveyed, European usage rates were at least 6% higher than the global average. For metrics related to time, coverage, risk, effectiveness, and efficiency, the gap widened to more than 14%. This reflects a culture of continuous process scrutiny—particularly around resource utilisation and delivery efficiency.

2. Risk and Coverage Metrics Valued More Highly

European respondents ranked risk and coverage quality metrics approximately 21% higher than the global average. The survey sample skewed toward Financial Services, Insurance, Healthcare, and Government—all heavily regulated sectors where risk measurement is a compliance requirement as much as an engineering discipline. The elevated scores may also reflect a stronger emphasis on protecting brand reputation in markets with sophisticated, demanding consumers.

3. Test Data Preparation Time Is a Distinctive Concern

European teams were 18% more likely to measure Time Spent Preparing Test Data than the global average, and valued the metric 23% more highly. The most plausible explanation is the General Data Protection Regulation (GDPR), which came into effect in May 2018 and placed significant restrictions on the use of production data in test environments. Teams forced to adopt data masking and synthetic data generation naturally began tracking the time cost of these new processes and monitoring their impact on overall pipeline throughput.

Asia Pacific: End-to-End Testing, API Quality, and a Widening Maturity Gap

The Asia Pacific region showed a distinct pattern: a stronger emphasis on end-to-end and API quality metrics, combined with a wider gap between DevOps leaders and laggards than seen elsewhere.

1. End-to-End Testing Metrics Are a Regional Priority

Asia Pacific respondents measured and valued end-to-end testing metrics more than their global peers. Percent of Automated End-to-End Tests was measured by 47% of regional organisations compared to 36% globally, and highly valued by 84% versus 70% globally. Risk Coverage showed an even larger gap: measured by 49% of Asia Pacific respondents versus 34% globally, and highly valued by 71% versus 59% globally. This focus aligns with the region’s strong emphasis on digital transformation and delivering seamless user experiences across complex, multi-step transactions.

2. API Testing Metrics Reflect Open Banking Trends

Overall, API quality metrics were measured by 16% more organisations in Asia Pacific than in the global sample. The highest-valued API metrics were API Test Coverage (63% regionally vs. 39% globally) and API Risk Coverage (79% vs. 62% globally). The majority of Asia Pacific respondents identified as working in Financial Services and Insurance—a sector undergoing rapid transformation through API-driven open banking initiatives, which places API reliability and risk coverage at the centre of quality strategy.

3. A Larger Gap Between DevOps Leaders and Laggards

The study classified respondents as DevOps leaders or laggards based on process maturity indicators. Asia Pacific had a lower proportion of DevOps leaders than the global average (18% vs. 26%). Regional leaders measured quality metrics at rates comparable to their global counterparts; regional laggards, however, measured quality metrics at substantially lower rates than laggards elsewhere. This bimodal distribution suggests that organisations that have genuinely invested in DevOps maturity in the region are competitive globally, while those that have not are falling further behind relative to both regional leaders and global peers.

Practical Implications for DevOps Teams

The global and regional findings converge on a consistent set of recommendations for teams that want to modernise their quality measurement practices:

• Audit your current metrics. Identify which metrics are counting artifacts (tests run, defects logged) and which are contextual (risk coverage, requirements coverage, end-to-end pass rate). Shift investment toward the latter.
• Define risk in business terms. Work with product owners and stakeholders to articulate what a bad release means for end users and revenue. Translate that definition into measurable quality signals.
• Expand coverage to integration and API boundaries. Team-level or application-level metrics hide failure modes that only appear at system integration points. Include end-to-end and API-level metrics in your go/no-go criteria.
• Address test data as a pipeline dependency. Especially in regulated industries or regions with data privacy obligations, test data preparation time is a real pipeline constraint. Measure it, and invest in synthetic data or masking tooling to reduce it.
• Use maturity benchmarks to set priorities. If your team is in the laggard category, the fastest path to improvement is adopting the measurement practices that leaders already use—not building entirely new tooling.

Ultimately, delivering better software faster requires more than CI/CD pipelines and test automation frameworks. It requires agreement—across engineering, product, and business stakeholders—on what quality and acceptable risk actually mean. The metrics follow from that agreement, not the other way around.

Source: Tricentis / Forrester Consulting, DevOps Quality Metrics that Matter (originally reported via cio.com, 2019).

Note: This article is based on a summary originally attributed to programmableweb.com. Pricing figures and product details reflect the original publication date of December 2017 and may have changed significantly. Always verify current pricing and feature sets on the vendor’s official website before making a purchase decision.

SmartBear ReadyAPI

ReadyAPI (formerly Ready API) is a commercial suite from SmartBear that bundles several specialized testing tools into one platform:

• SoapUI Pro – Despite the name, this tool handles REST, SOAP, and web service APIs. It supports recording and replaying requests, building full test suites, importing Swagger or RAML description files to accelerate test creation, and running tests via command-line for CI integration.
• LoadUI Pro – A dedicated load and performance testing tool. It lets testers vary data volumes and simulated user counts to assess API scalability.
• Secure – A security testing tool specifically designed for API endpoints.
• ServiceV – Supports creation, deployment, and management of virtual API environments, useful when a dependent service is unavailable during testing.
• AlertSite – An API monitoring component that tracks usage and behavior across development, test, and production environments.

ReadyAPI is a strong choice for teams that need an integrated platform covering functional, performance, security, and monitoring testing under one license. A free, non-commercial version of SoapUI (without the “Pro” features) is available as an open-source download.

Postman

Postman began as a free Chrome browser extension and evolved into one of the most widely used standalone API testing tools available. Its core functionality allows testers to store, organize, and execute API calls. Calls can be chained together so that each request is predicated on the response from the previous one, which is essential for testing stateful workflows.

Key capabilities in paid tiers include:

• Shared team collections for collaborative testing
• API monitoring
• Environment variable management across multiple workspaces
• Dedicated product support

Postman is particularly well suited for teams that want a GUI-driven workflow with a low barrier to entry. Its free tier is generous enough for individuals and small teams, and the paid tiers scale to enterprise needs. Note that pricing and plan names have changed considerably since 2017—check postman.com/pricing for current details.

Airborne (Ruby Library)

Airborne is a free, open-source Ruby library for testing REST APIs. Unlike GUI-driven tools, Airborne is a code-first solution: tests are written in Ruby using the RSpec convention, giving developers full programmatic control over test logic.

Common assertion helpers include:

• expect_json – Assert specific JSON values in the response body
• expect_json_types – Assert the data types of JSON fields
• expect_status – Assert the HTTP status code of the response

Because tests are plain Ruby code, integrating Airborne with a CI/CD pipeline is straightforward—any system that can run a Ruby process can execute Airborne tests. The library is installed via RubyGems. Teams already working in Ruby will find the learning curve minimal; those unfamiliar with Ruby may prefer a GUI-based alternative.

Command-Line Tools: curl and HTTPie

Both curl and HTTPie are free, open-source command-line utilities for making HTTP requests. They are not designed for building large automated test suites, but they are invaluable for:

• Rapidly exploring an unfamiliar API during development
• Issuing one-off requests to isolate a suspected bug
• Scripting simple smoke tests in a shell pipeline

Both tools support basic authentication and token-based auth. HTTPie is often preferred for its more human-readable output and intuitive syntax, while curl is nearly universally available and extremely well documented. For quick exploratory work or lightweight CI checks, either tool is a practical starting point that requires no additional installation in most Unix environments.

API Science

API Science is a commercial monitoring and testing tool aimed at APIs running in production. Its strengths are operational visibility rather than pre-release functional testing:

• Quick setup and low configuration overhead
• Monitoring of third-party and internal APIs
• Alerting when an API degrades or fails
• A unified dashboard showing health and usage metrics

API Science is best suited for teams that need continuous visibility into production API behavior rather than teams focused purely on pre-release testing. Pricing as of the original article started at $29 per month with a free 30-day trial; verify current plans directly with the vendor.

API Metrics

API Metrics is another production-focused tool, but with an emphasis on performance rather than general monitoring. Instead of virtualized calls, it executes real authenticated requests with actual data against a live API and collects the resulting performance data. This gives development teams accurate, real-world latency and throughput measurements rather than synthetic estimates.

API Metrics offers self-serve, supported, and managed service tiers. Contact the vendor for current pricing. This tool is most relevant for teams managing SLA obligations or troubleshooting production performance regressions.

How to Choose the Right Tool

Before selecting a tool, define the problem you are trying to solve. The following questions can help narrow the field:

• What kind of testing do you need? Functional correctness, load/performance, security, or production monitoring each calls for different tooling.
• Where does testing happen? Pre-release testing in a CI pipeline has different requirements than continuous monitoring in production.
• What is your team’s technical profile? Developers comfortable with Ruby or scripting languages may prefer code-first tools like Airborne or curl. Teams with mixed skill sets may benefit from the GUI of Postman or ReadyAPI.
• What are your collaboration and reporting needs? If multiple team members must share test collections or review results, look for tools with team features and dashboards.
• What is your budget? Open-source options (Airborne, curl, HTTPie, SoapUI community edition) cover many use cases at no cost. Commercial suites add polish, support, and integrated monitoring.

No single tool covers every scenario perfectly. Many teams use a combination: curl or HTTPie for quick exploration, Postman or Airborne for functional test suites, and a dedicated monitoring tool like API Science or AlertSite for production observability.

Summary Comparison

The API testing landscape continues to evolve rapidly. Evaluate tools against your team’s current workflow, and revisit your toolset periodically as your APIs and organizational needs grow.