TECHNOLOGY SERVICE · 10
QA & Test Automation
Test strategy and automation that catches regressions before your customers do. For teams whose release nights run long, whose manual test cycle has become the bottleneck, or who have a suite everyone has learned to ignore.
Test the money paths first
Coverage percentages are a poor guide to risk. Work starts by ranking flows on business impact and failure history: checkout, payment posting, permission checks, the report the regulator reads. Those get automated first and run on every change, while low-traffic administrative screens can stay manual for now.
The ranking is agreed with your product owner, so a difficult trade-off is a shared decision rather than a QA preference.
A pyramid, not an ice cream cone
Suites made almost entirely of browser tests are slow, brittle and expensive to keep. Most behaviour is better verified at unit and service level, with a thin layer of end-to-end journeys covering what only a real browser can prove. Rebalancing an existing suite usually cuts run time sharply while catching more.
- Unit and service tests carrying the bulk of assertions
- A small, stable set of end-to-end journeys
- Contract tests where teams or suppliers integrate
Flaky tests are worse than no tests
A suite that fails at random teaches everyone to re-run it, and after that it protects nothing. Flakiness is treated as a defect with an owner: fixed waits are replaced, test data is isolated per run, and any test that cannot be stabilised is quarantined and rewritten rather than left to erode trust.
Performance and security in the same pipeline
Load profiles are modelled on real traffic, including the seasonal peaks that matter here, and run against a production-like environment before major releases. Dependency scanning and a baseline application security scan run on every build, so the common vulnerabilities are found by the pipeline rather than by an auditor.
What you get
- Risk-based test strategy agreed with your product owner
- Automated regression suite running in CI on every pull request
- End-to-end journeys covering the highest-value flows
- Performance test scripts with an agreed baseline and thresholds
- Dependency and application security scanning in the pipeline
- Test data management approach that keeps runs isolated
- Defect triage process with severity definitions
- Quality dashboard covering pass rate, run time and escaped defects
Typical outcomes
<15 min
Target pipeline run time for the core suite
70%+
Manual regression effort removed
<2%
Flaky test rate we hold suites to
Stack we use
Questions
No. Automation pays where a test runs often and the flow is stable, while exploratory testing remains better for new features and unusual paths. A sensible split is part of the strategy we agree at the start.
That is a common brief. The first two weeks measure flakiness and run time, quarantine the worst offenders and rebalance the pyramid, which usually restores confidence before any new coverage is added.
Yes, through our staffing model, including bilingual testers for Arabic and English interfaces. Most teams do best with a small permanent QA presence supported by automation engineers during a push.
Suites are built to run in whatever CI you already use rather than requiring a change of tooling. Where the pipeline itself is the constraint, we will say so and scope that work separately.
Related
Next step
Start with a 20-minute call.
Tell us the roles you need filled, the system you need built, or both. You will speak to someone who has done the work, and leave the call with a route forward.