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Test automation

A working digital service keeps changing, so we automate the checks that protect it every time code changes.

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Problem

01

When every release needs the same manual checks, releases slow down and testers burn out. Teams start releasing less often to avoid the effort, so each release gets bigger and riskier.

Outcomes

  • Automated checks on every change, with fast feedback to developers.
  • Regressions caught before they reach users.
  • Tests that are reliable, maintained and trusted by the team.
  • Testers freed for exploratory and accessibility work.
  • Data pipelines and AI outputs tested, not assumed.

Scope

In scope

  • Automation strategy and framework set-up.
  • Unit, API and user-interface test automation.
  • Integration of tests into delivery pipelines.
  • Test data management for automated tests.
  • Maintenance approach and team training.

Not included

  • Writing application features. We automate the tests that check them.

Data and AI quality assurance

Data pipelines and AI features can fail quietly: a field changes meaning, a model drifts, an answer is confidently wrong. We test data pipelines and AI outputs for accuracy, bias and drift, using agreed measures and test sets.

  • Data quality checks inside pipelines.
  • Evaluation test sets for AI outputs, including difficult cases.
  • Monitoring for drift, with alerts to a named owner.

Approach

  1. Discovery

    We pick the checks that are repeated most and matter most. We also review how code gets from developer to live.

  2. Delivery Planning

    We plan the framework and where tests run in the pipeline. We agree how a failing test blocks a release and how exceptions are approved.

  3. Build and Test

    We build the framework and the automated test suites, run them on every change, and fix flaky tests fast. For data and AI, we add data checks and evaluation test sets. Before go-live, you get the test results, and a named person decides whether to go ahead.

  4. Operational Readiness, Hypercare and Closure

    We hand over to your team and train them to extend and maintain the tests. Where data and AI are involved, we set up monitoring for drift. We provide extra hypercare for the first few weeks, then close the work with your sign-off.

Deliverables

Automation strategyTest framework and automated test suitesPipeline integration and reportingTest data approachFor data and AI: data checks, evaluation test sets and drift monitoring

Technologies

Test frameworks: Playwright, Cypress or Selenium for user interfaces; Postman or code-based tests for APIsPipelines: Azure DevOps and GitHub ActionsCode quality: static analysis such as SonarQube

Controls

A failing test blocks release unless an exception is approved and recorded.Flaky tests are fixed or removed within an agreed time.Automated tests use synthetic or anonymised data.AI evaluation results are recorded with the test set used.

Frequently Asked Questions

How long before automation pays off?

Automating a handful of the most repeated checks often helps within weeks. Wider coverage builds up over time.

Our automated tests keep failing for no reason. Can you fix them?

Yes. We find why they are unreliable, fix or remove them, and set rules so new tests stay stable.

Can automation replace our testers?

No. It takes over repeated checks, so testers can spend time on exploratory and accessibility testing.

How do you test AI features?

We use evaluation test sets, including difficult and adversarial cases, and record results against agreed measures.

Releases held up by manual testing?

A Technical Audit reviews your test automation and delivery pipeline in 10 to 15 working days and gives you a prioritised remediation plan.

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