You’re in good company

A few of the companies we’ve worked with.

When testing holds releases back

When most testing is done by hand, every release waits for it. Releases become less frequent and larger, and a larger release is harder to check and harder to put right when something breaks. Automated tests help only if the team trusts them: tests that fail at random get ignored, and tests that miss real problems give false comfort.

Tests in the right places

Small tests check one piece of code at a time. They run quickly and point to the exact place that broke, so most checks belong there. Contract tests check that your services still agree on how they work together, without starting them all at once. End-to-end tests use the whole product the way a customer does, which makes them slower and more likely to fail for reasons that have nothing to do with the change. We keep those for the journeys that matter most, such as sign-up, login and checkout.

What we set up with your team

The tests, the pipeline that runs them and the habits that keep them useful.

  • A testing plan

    What to test, where and how often, agreed with your team, so each new test goes in the right place.

  • Checks on every pull request

    Fast tests on each proposed change, with results your developers see before it’s merged.

  • Key journeys, end to end

    Sign-up, login, checkout and your other key journeys, tested in a real browser before each release.

  • Contract tests between services

    Each service checked against what the others expect from it, so a breaking change is caught before it’s deployed.

  • Flaky tests fixed

    Unreliable tests found from their history, set aside so they don’t block releases, fixed at the cause and put back.

  • Realistic test data

    Data that behaves like the real thing, without copying your customers’ personal details.

  • Screens checked for changes

    Each screen compared with its last approved version, so an unintended change to the layout is spotted before release.

  • People exploring the product

    Testers using the product the way your customers would, looking for what the automated tests don’t cover.

  • AI features tested on real questions

    Answers checked against what a good answer must include and avoid, whenever the model, its instructions or its data change.

Coverage isn’t the whole story

Code coverage shows which lines ran during your tests. It doesn’t show whether any test would fail if one of those lines were wrong. Mutation testing checks that. It makes small, deliberate changes to the code, such as removing a check or reversing a condition, and runs the tests again. If they all still pass, nothing may be checking that behaviour. We use it where a mistake costs most, such as pricing, permissions and payments.

RuleChanged

Only a manager can approve a refundAnyone can approve a refund

Tests

  • A manager can approve a refund: passes
  • Staff can’t approve a refund: fails

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Tests that fail for a reason

A flaky test sometimes fails on code that hasn’t changed. It teaches the team to run it again until it passes, and before long a real failure gets the same treatment. We find unreliable tests from their history and set them aside, so they don’t hold up releases. Then we fix the cause, such as timing, shared data, the order tests run in or a service outside your control, and put them back.

When AI writes the code, and the tests

Teams that use AI assistants more tend to deliver changes faster, so the tests that check each change matter more. They can draft tests quickly too. Read those carefully: a test written from the code checks that the code does what it does today, mistakes included. Good tests start from what the software is meant to do. And when a tool repairs a failing test on its own, check the repair: a test that fails because the product is wrong shouldn’t be changed to pass.

A test written from the code checks that the code does what it does.

Start with what you check by hand

We start by looking at how you test today: what runs, how long it takes, which tests fail at random and which key journeys have no tests at all. You get a short plan, ranked by the time and risk each step saves. Then we automate the first journeys and fix the worst flaky tests with your team, so the very next release benefits.

Work with us as a dedicated team, on a fixed price project or a mix of both. Compare engagement models →

Questions worth asking

What should we automate first?

The journeys that cost most when they break, and the checks your team repeats before every release.

What coverage should we aim for?

No single number suits every codebase. Coverage is useful for finding code with no tests at all, but a high figure doesn’t show that the tests would catch a mistake.

Can AI write our tests?

It can draft them quickly. Each one still needs a careful read, to check it tests what the software should do rather than what it does today.

Our tests take too long. Can they run faster?

Often, yes. Common causes are too many end-to-end tests, tests that wait a fixed time instead of checking, and tests that can’t run side by side.

Our old system has no tests. Where do we start?

With tests that record what it does today, so every change after that can be checked against them. Legacy Modernisation →

Can you work with our QA team?

Yes. We work alongside your testers and developers, and leave tests and standards they can keep using.

Do you test for security too?

Some security checks run in the same pipeline. For the rest, see Application Security →

Let’s build it better

Tell us how you test and release today. We’ll look at where the time goes and suggest a sensible first step.

Thanks, we’ll be in touch soon.

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