Manual testing slowing releases
Each release requires lengthy manual regression cycles.
05 / Technology & Engineering
Automation Testing and Quality Engineering
Quality problems become more expensive the later they are found. With AI in the mix, testing also has to cope with behaviour that is probabilistic rather than exact. We build quality engineering practices that catch defects early, test AI systems meaningfully and give teams confidence to release often.
At a glance
- Service
- Automation Testing and Quality Engineering
- Pillar
- Technology & Engineering
- Typical starting point
- Quality and test maturity assessment
- Typical first phase
- 2 to 3 weeks for assessment; automation foundation in 6 to 10 weeks
- Key technologies
- MLOps, Large Language Models, Intelligent Automation
Durations depend on scope, data readiness and integration needs.
What is Quality Engineering?
Short answer
Automation testing and quality engineering is the practice of building quality into software delivery through automated functional, integration, performance and security tests, quality gates in CI/CD pipelines, and, for AI systems, evaluation datasets and behavioural tests that measure output quality, robustness and safety.
Quality engineering goes beyond testing at the end of a project. It shapes how requirements are written, how code is designed for testability, which tests run automatically on every change and which quality measures must be met before release.
AI systems add new challenges. A language model may give different but equally valid answers to the same input, or a subtly wrong answer that passes simple checks. RPA bots and automations fail when target applications change. We extend quality engineering to these systems with evaluation sets, property-based and behavioural tests, model-graded evaluation where calibrated, regression suites for prompts and monitoring in production.
Business problems
What problems does Quality Engineering solve?
These are the situations where organisations most often find this service worthwhile.
Defects found by users
Problems reach production because testing is incomplete.
Flaky automated tests
Existing test suites fail unpredictably and are ignored.
No way to test AI behaviour
Teams cannot tell whether prompt or model changes improve or worsen quality.
Performance problems under load
Applications slow down or fail when usage grows.
Broken bots after application updates
RPA and integrations fail when connected systems change.
Use cases
Where can Quality Engineering be used?
Representative applications. The right use case depends on your processes, data and constraints.
| Area | Example application |
|---|---|
| Web and mobile apps | Automated UI, API and end-to-end tests integrated into CI/CD. |
| APIs and integrations | Contract, integration and data validation tests. |
| AI and LLM applications | Evaluation suites, regression tests for prompts, safety and injection tests. |
| ML models | Data validation, model performance tests and fairness checks before deployment. |
| RPA and automations | Regression testing of bots against target application changes. |
| Performance | Load, stress and soak testing for applications and AI inference services. |
Who should consider it
- Teams releasing slowly because of manual testing
- Organisations building LLM or ML applications
- Businesses with growing RPA and automation estates
- Product teams scaling their user base
How AARHIT approaches it
How we deliver Quality Engineering
Each stage ends with something you can review, so decisions are made on evidence rather than on promises.
Stage 1 of 6
Quality assessment
We review current testing practices, tools, coverage, defect patterns and release processes.
Output: quality maturity assessment and priorities.
Stage 2 of 6
Test strategy
We define the balance of unit, integration, end-to-end, performance, security and AI evaluation testing.
Output: test strategy and quality gates.
Stage 3 of 6
Framework setup
Test frameworks, data management and environments are set up for reliable automation.
Output: maintainable test framework.
Stage 4 of 6
Test automation
Priority test suites are automated and integrated into CI/CD pipelines.
Output: automated suites running on every change.
Stage 5 of 6
AI evaluation and testing
Evaluation sets, behavioural tests and safety tests are built for AI components.
Output: AI quality suite and dashboards.
Stage 6 of 6
Quality reporting and enablement
Quality metrics are reported and teams are trained to maintain and extend tests.
Output: quality dashboards and trained teams.
Technology
What technologies are involved?
We choose tools on fit, cost, security and your existing estate. These are typical options, not fixed commitments.
Learn more about the underlying technologies
| Layer | Typical options |
|---|---|
| UI and end-to-end | Playwright, Cypress, Selenium, Appium |
| API and contract | Postman, REST Assured, Pact, pytest |
| Performance | k6, JMeter, Locust, Gatling |
| AI testing | promptfoo, DeepEval, Ragas, custom evaluation suitees |
| Security | OWASP ZAP, dependency and container scanning |
| Pipelines | GitHub Actions, GitLab CI, Azure DevOps, Jenkins |
Outcomes
Potential business outcomes
Targets are agreed with you during scoping and measured against a baseline. We do not quote improvement figures before we have seen your process and data.
- Faster, more confident releases
- Fewer defects reaching users
- Measured quality of AI outputs over time
- Early detection of regressions in bots and integrations
- Applications that perform under expected load
How long does implementation generally take?
Typical timeline
A quality assessment usually takes 2 to 3 weeks. Establishing a test automation foundation with priority suites typically takes 6 to 10 weeks. Coverage then grows as part of normal delivery.
What determines complexity?
- Application size and architecture
- Test data availability
- Stability of test environments
- Number of AI components
- Integration with legacy systems
Implementation
Implementation considerations and risks
Most difficulties in this kind of work are predictable. Planning for them early keeps delivery on track.
Automate the right tests
Not everything should be automated. Focus on stable, high-value, frequently repeated checks.
Invest in test data
Reliable, privacy-safe test data is often the biggest obstacle to automation.
Fix flaky tests promptly
Tests that fail randomly erode trust and are soon ignored.
Accept variability in AI outputs
Test AI behaviour against criteria and ranges rather than exact matches.
| Risk to consider | How we manage it |
|---|---|
| High test maintenance effort | Good framework design, stable selectors and page object patterns. |
| False confidence from shallow tests | Risk-based test design and coverage of critical paths. |
| Sensitive data in test environments | Synthetic or masked data and environment access controls. |
| AI regressions unnoticed | Evaluation suites run on every prompt, model or data change. |
FAQ
Questions about Quality Engineering
What is quality engineering?
Quality engineering builds quality into the software delivery process through practices such as testable design, automated testing, quality gates and continuous measurement, rather than relying only on testing at the end.
How do you test AI applications?
By creating evaluation datasets with expected outcomes or grading criteria, measuring quality, grounding and safety automatically where valid, using calibrated model or human grading for open-ended outputs, and running these checks whenever prompts, models or data change.
Which test automation tool is best?
It depends on your technology stack and team skills. Playwright is a strong choice for modern web applications, Appium for mobile, and k6 or JMeter for performance testing. We recommend based on your context.
Can you test RPA bots?
Yes. We create regression tests that run bots against test environments, verify outcomes and detect failures caused by changes in target applications.
How much test automation coverage do we need?
Coverage targets should follow risk. Critical business flows and frequently changed areas deserve strong automated coverage, while rarely used, stable features may need less.
Next step
Considering Quality Engineering for your organisation?
Share the process, product or decision you have in mind. We will ask the right questions, outline options and suggest a sensible first step.