QA & Test Automation.
A test suite the team trusts enough to act on.
Drema builds test automation that teams actually rely on. The measure is not coverage percentage but whether a green run gives real confidence to release, and whether a red run means something is genuinely broken. A flaky suite is worse than no suite, because it teaches everyone to ignore failures.
A test suite the team trusts enough to act on.
What usually
goes wrong.
The pipeline takes 40 minutes and fails at random, so people rerun it until it passes. Coverage looks respectable, yet regressions still reach customers, because the tests exercise implementation details rather than the journeys that matter.
Everything that ships
with this work.
Not a menu to choose from. Each piece is here because leaving it out is what makes this kind of project fail six months later.
Test strategy
Deciding what belongs in unit, integration and end-to-end layers, so the suite is fast where it can be and thorough where it must be.
End-to-end automation
Reliable browser tests covering the critical journeys, written to be resilient rather than brittle.
Flake elimination
Root-causing intermittent failures — timing, ordering, shared state — rather than adding retries that hide them.
CI integration
Parallelised runs with useful failure output, fast enough to block a merge without stalling the team.
Performance testing
Load profiles based on real traffic, with thresholds that fail the build on regression.
Accessibility testing
Automated WCAG checks in the pipeline plus manual keyboard and screen-reader passes.
The order matters more
than the tools.
Most of what separates a project that lands from one that stalls is sequence. This is the order we work in, and why each step comes where it does.
- 01
Find what actually breaks
We review past incidents and support tickets. Real failure history is a better guide to test priorities than a coverage report.
- 02
Cover the critical journeys
The handful of flows where a failure costs money get end-to-end coverage first.
- 03
Push tests down
Move as much verification as possible to fast unit and integration levels, keeping end-to-end for genuine integration risk.
- 04
Make it fast and stable
Parallelisation, isolated data and deterministic setup, so nobody learns to ignore a red build.
- 05
Hand over
Patterns and documentation so your developers extend the suite naturally as they build.
Where this gets
put to work.
The situations this service is built for. If one of these sounds like your problem it is worth a conversation — and if none of them do, say so on the call and we will point you at what would actually fit.
Pre-release confidence
Knowing a deploy is safe without a manual regression pass that takes two days.
Rescuing a flaky suite
Turning an ignored, unreliable pipeline back into a trusted signal.
Regression safety for refactoring
The coverage that makes large-scale change survivable.
Launch load testing
Establishing real capacity limits before a campaign rather than during it.
Have a use case that is not on this list? That is usually the interesting one.
Chosen to fit,
not to impress.
We pick tools that suit the problem and that your team can maintain after we hand over — never to pad a capability list.
Questions we
get asked.
Straight answers, including the ones that talk you out of work we would otherwise be paid for.
What test coverage percentage should we aim for?
Coverage percentage is a poor target — it is easy to reach a high number while testing nothing that matters. We prioritise by risk: the journeys that cost money when they break get thorough coverage, and trivial code does not need a test written to satisfy a metric.
Our tests are flaky. Can you fix them?
Yes, and it is a common engagement. Flakiness almost always traces to timing assumptions, shared state between tests or non-deterministic data. We fix the causes rather than adding retries, because retries hide real intermittent bugs.
Should we hire manual testers?
Manual testing is valuable for exploratory work, usability and judgement-based checks that automation cannot make. It is a poor use of people for repetitive regression passes. The right mix is automation for repetition and humans for judgement.
How long does it take to see benefit?
Covering the critical journeys typically takes three to six weeks and delivers most of the value. Full test strategy adoption is ongoing, but you should not need to wait for it to start releasing with more confidence.
Can you test an application you did not build?
Yes, that is the majority of this work. We start from failure history and user journeys, which needs no prior familiarity with the codebase, and the tests themselves become useful documentation of how the system behaves.
Does this slow down development?
Initially the suite costs time to write; after that it saves considerably more. The condition is that the suite stays fast and reliable, which is precisely why we treat speed and flakiness as first-order requirements.
Talk it through with a founder.
Bring the actual problem. You will get a straight answer on whether qa & test automation is the right approach
— including when it is not.
