Software Quality Metrics CTOs Should Track in 2026

Liviu Lupei

Founder & Solutions Architect, Endtest · August 7, 2026

CTOs don't need another dashboard with 37 QA metrics.

They need to know something much simpler:

Can we ship quickly without breaking things?

That's really the job of a quality strategy.

And yet, a lot of engineering organizations still measure QA using numbers that are easy to collect but don't tell you very much.

Number of test cases. Lines of automation code. Percentage of tests automated. Number of bugs found.

Those numbers can all go up while your ability to ship reliable software gets worse.

In 2026, especially as AI accelerates how quickly teams can build software, I think CTOs need a different scorecard.

Here are the metrics I'd pay attention to.

1. Escaped defects

The obvious one.

How many meaningful bugs make it to production?

Not every bug deserves equal weight, of course. A spacing issue in an admin page isn't the same as customers being unable to complete checkout.

So rather than obsessing over the raw number, categorize production defects by severity and affected functionality.

The interesting trend is whether important regressions are becoming less common over time.

If your automated test suite grows from 2,000 tests to 8,000 tests while the number of serious production regressions stays the same, something isn't working.

Test count is an input. Escaped defects are an outcome. CTOs should care much more about the latter.

2. Critical functionality coverage

Code coverage is useful to engineers.

It's much less useful as an executive quality metric.

You can have 90% code coverage while having no automated test that verifies whether a customer can actually sign up, upgrade their subscription, reset their password, or complete checkout.

What I would rather see is a simple map of your important product functionality and the automated tests covering it.

Something like:

The traditional QA term for this is a Requirements Traceability Matrix, or RTM.

But you don't need to turn it into an enterprise bureaucracy project.

For most SaaS companies, a lightweight feature-to-test coverage matrix is enough.

The point is that you should be able to look at the product and answer:

What important functionality isn't protected by automated tests?

That's much more useful than knowing you have 4,312 tests.

3. Can test automation keep up with the sprint?

This might be my favorite metric.

Imagine your engineering team finishes 15 meaningful product changes during a sprint.

How many of those changes have the appropriate automated tests by the end of that sprint?

If the answer is five, your test automation isn't really keeping up with development.

You're accumulating quality debt.

And eventually someone schedules a "QA automation sprint" to catch up.

Then development continues.

Then QA falls behind again.

I've seen organizations focus on how many Selenium or Playwright scripts their automation engineers produce. Sometimes they'll even indirectly measure productivity through commits or lines of code.

I think that's backwards.

I'd replace the KPI

How much automation code did we write?

with

Are we continuously keeping the product covered as it changes?

A great automation engineer who writes very little code but keeps every important sprint feature covered is doing a better job than someone who builds thousands of lines of framework code while the test backlog keeps growing.

This is also where tooling choices start to matter.

If creating a test requires writing a few hundred lines of code, debugging locators, configuring dependencies, reviewing pull requests, maintaining CI infrastructure and keeping framework versions working, you're spending a lot of your automation capacity on the automation itself.

That's time that isn't being spent increasing product coverage.

4. Test maintenance cost

Automated tests are often described as if they have two states:

Created.

Not created.

But creation is only the beginning.

Every automated test becomes something you own.

The UI changes. An API changes. A locator stops working. Authentication changes. A browser update behaves differently. A dependency breaks. A test becomes flaky.

Someone then has to figure out whether the product is broken or the test is broken.

This is why I'd track the engineering hours spent maintaining automated tests.

Not perfectly. You don't need everyone running timers.

Even a rough monthly estimate can be eye-opening.

If three engineers spend a combined 100 hours every month maintaining your test infrastructure and fixing test scripts, that's part of the cost of your test automation strategy.

Which leads to another metric CTOs should care about.

5. The true cost of test automation

Selenium is free. Playwright is free. And both are excellent open-source projects. But your test automation isn't free.

Someone has to build the framework. Someone has to maintain it. Someone has to handle reporting, parallel execution, test data, browser infrastructure, retries, screenshots, logs, CI integrations, authentication, secrets, dependency upgrades, browser updates and flaky tests.

And eventually, someone has to understand the framework built by the person who left the company two years ago.

The license cost of your testing library might be $0 while the actual cost of operating your test automation program is tens of thousands of dollars per year.

This is the same mistake companies sometimes make when comparing an enterprise testing platform with an open-source library.

They compare:

$0 vs. $15,000

when the real comparison might be:

$90,000 in engineering time and infrastructure vs. $15,000 for software that eliminates most of that work.

The exact numbers will obviously vary by company.

But CTOs should be measuring total cost of ownership, not license price.

A tool is expensive when it consumes a lot of your team's time, not when it has a price.

6. Time to understand a failed test

A test failing is only useful if someone can understand why it failed.

If a test fails at 2:13 AM in CI and an engineer spends 45 minutes the next morning looking through logs, screenshots and source code trying to determine what happened, that investigation time is part of your QA cost.

Multiply it across hundreds of failures.

This is one reason flaky tests become so damaging.

Eventually the team stops treating a red test as information.

They treat it as noise.

"Probably flaky. Run it again."

At that point, you technically have automated testing.

But you don't have much release confidence.

I'd track how quickly the team can go from:

Test failed

to:

We understand what happened.

AI can be particularly useful here because screenshots, page source, test context and error information can be analyzed together instead of requiring someone to manually reconstruct the failure.

The goal is to save engineering time, not to make an impressive AI demo.

7. Team adoption

Here's another underrated one:

Who can actually create and maintain your automated tests?

If your 50-person engineering organization has one automation engineer who understands the Selenium framework, you don't really have broad test automation adoption.

You have a dependency on one person.

This creates two problems.

First, that person becomes a bottleneck.

Every sprint produces functionality faster than one person can possibly automate it.

Second, the system becomes fragile organizationally.

What happens when that person is on vacation? Or moves to another team? Or leaves?

I think a healthy automation program should gradually increase the number of people capable of contributing to it.

That can include QA engineers, developers, product people and other technical team members.

This is one of the ideas behind the Endtest AI Test Creation Agent.

Instead of requiring every contributor to understand a testing framework, selectors, browser drivers and all of the code surrounding the test, they can describe the scenario and generate an editable automated test.

The resulting test is a set of understandable steps that the team can inspect, modify and reuse, rather than something hidden inside a giant AI-generated codebase.

That makes team adoption a much more realistic KPI.

8. Release confidence

This one isn't as easy to put into a database column.

But it's arguably the metric everything else is trying to improve.

Before a deployment, ask the team:

How confident are we that this release hasn't broken an important customer workflow?

If the answer depends on whether Sarah manually clicked around staging for two hours, you have a problem.

If the answer is:

"We automatically tested every critical workflow affected by this release across the environments we care about, and everything passed."

That's a very different situation.

The point of automated testing is confidence, not the automation itself.

The metrics I wouldn't obsess over

There are a few numbers I'd treat carefully.

Lines of test automation code.

More code means more code to maintain. It is not a measure of quality.

Number of automated tests.

10,000 bad tests aren't better than 1,000 meaningful tests.

Percentage of tests automated.

Automating low-value tests just to increase this number doesn't help anyone.

Number of bugs discovered by QA.

A QA team finding fewer bugs can mean quality improved. Or it can mean they're missing bugs. The number alone doesn't tell you much.

Automation engineer output.

Counting scripts, commits or tickets completed encourages the wrong behavior. Measure whether automation coverage keeps pace with product development instead.

A better CTO quality dashboard

If I were building a simple quality dashboard for a CTO, I'd probably keep it to something like this:

• Serious production defects
• Critical functionality covered by automation
• Sprint features receiving automated coverage
• Flaky test rate
• Test maintenance effort
• Time to understand failures
• Total cost of automation
• Number of people actively contributing tests

You can add more metrics later.

But those eight already answer some pretty important questions.

Are we protecting the right things? Are we keeping up with development? Does the team trust the tests? And how much are we actually spending to get that confidence?

The goal isn't more test automation

Your company isn't in the business of producing Selenium scripts. Or Playwright scripts. Or even Endtest tests.

You're trying to build a product and ship improvements to customers without constantly breaking what already works.

Testing infrastructure should make that easier.

When the testing framework itself becomes a significant engineering project, with its own architecture, dependencies, infrastructure, maintenance backlog and a handful of people who understand it, you should include all of that in the ROI calculation.

Modern testing platforms can remove a lot of that framework tax.

With Endtest, for example, teams can create and run automated tests without maintaining their own browser automation framework, while AI can accelerate test creation and help with maintenance and failure analysis.

That doesn't make Selenium or Playwright bad tools.

It just means "free" isn't the metric a CTO should optimize for.

There are better questions. How quickly can we cover new functionality? How much does that coverage cost us? Can the whole team contribute? And when the product changes again next week, can our automated tests keep up?

And if you're comparing the cost of building and maintaining your own automation stack with using a managed platform, don't just compare license prices.

Compare the actual resources required to get the same outcome.

You can see how Endtest pricing compares once engineering time, infrastructure and ongoing maintenance are part of the equation.