The AI augmented QA engineer

While most of the focus in QA has tended to go to test automation in recent years, functional testing still plays a critical role in ensuring that software behaves as expected. While applications and IT ecosystems are growing more complex, traditional functional QA is being put under pressure.

To combat this, Gen AI can be a powerful tool in your QA engineers’ kit. So how exactly can an AI-augmented QA engineer outshine their traditional counterparts?

1. Smarter Test Case Generation

Taking in new requirements and creating test cases from scratch can often be time-consuming and repetitive. AI can analyze requirements, user stories, and historical defect data to automatically generate relevant test cases.

By utilizing special fit-for-purpose AI models, all industry knowledge and best practices are automatically incorporated: bodies of knowledge, test techniques, latest insights, intelligent preconfigured prompts, etc.

Utilization of such tools decreases manual effort and ensures broader, more consistent coverage across the application, while the QA engineer can focus on using their business knowledge to review and finetune scripts for optimal efficiency.

2. Enhanced Risk-Based Prioritization

Provided the right data is available, AI can help identify which functional areas are most prone to failure. By analyzing usage patterns, code changes, and past incidents, potential failure hotspots can be found. This allows QA engineers to enhance their risk-based approach and focus their effort where it matters most, while freeing up time for other tasks such as targeted exploratory testing.

3. Improved Requirement Traceability

Something tedious and often overlooked is traceability and test coverage. AI can assist the functional tester in mapping test cases to functional requirements and highlight potential coverage gaps or any other inconsistencies. This ensures that all business-critical functionality is covered and there are no blind spots in the application coverage.

4. Enhanced Exploratory Testing

If production data is available, AI can analyze user behavior and system logs, and suggest exploratory paths that are unknown to the team or may not be obvious through manual analysis. This supports testers in stepping away from only looking at existing requirements and provides them with real-use insights, uncovering potential edge cases and behaviors that standard tests might miss.

5. Continuous feedback and fine tuning

Because AI enables real-time analysis of test results, it helps the team in quickly identifying regressions and adapting their test strategies. If the model detects an uptick in issues in a particular area, it can guide the tester to dedicate more time on it, explore additional cases, etc. Inversely, the AI model might optimize the tester’s time by decreasing the coverage in historically issue-free areas of the application. Small tweaks like this can be a big difference maker in tester efficiency.

To summarize with a cliché that has been overly popular lately: Generative AI won’t replace functional testers – but AI-augmented functional testers will replace traditional manual testers, who stick to their old way of working.
The QA engineers who understand how to get the most out of Gen AI will outshine their colleagues in speed, efficiency, and quality of work.

PS: Take a look at our Gen AI Amplifier by Sogeti  . Accumulating all of our in-house best practices and knowledge into a one-stop-platform to augment our QA professionals. Our Gen AI amplifier speeds up test creation, optimizes coverage, maximizes tester efficiency, and ultimately boosts application quality.

Shift-Left Testing: Building Quality from the Start

Shift-Left Testing moves testing earlier in the development lifecycle. It focuses on preventing defects through early collaboration, automation, and continuous feedback.

Key practices include:

– Test-Driven Development (TDD) and Behavior-Driven Development (BDD)

– Static code analysis and linting

– Unit, integration, and API testing

– Automated testing in CI/CD pipelines

Benefits include faster feedback, reduced defect costs, and improved collaboration between developers and testers.

Shift-Right Testing: Validating in the Real World

Shift-Right Testing focuses on testing in production or production-like environments. It emphasizes observability, resilience, and user experience.

Key practices include:

– Real User Monitoring (RUM) and Synthetic Monitoring

– Canary deployments, A/B testing, and feature toggles

– Chaos engineering to test system resilience

– Continuous feedback loops from production to development

This approach ensures performance under real conditions and enables faster recovery and improvement.

Why You Need Both

Shift-Left helps you build the right product; Shift-Right ensures it works well in the real world. Together, they form a continuous quality loop:

1. Prevent defects early with Shift-Left.

2. Validate and learn from production with Shift-Right.

3. Feed insights back into development for continuous improvement.

This dual strategy is essential for Agile, DevOps, and CI/CD environments.

The Modern Quality Engineer

Today’s Quality Engineers strategically enable quality across the lifecycle. They work across the pipeline, from code to customer, using automation, data, and collaboration to drive excellence.

Key skills include:

– Test automation and scripting

– Observability and monitoring tools

– CI/CD and DevOps practices

– Data analysis and user experience insights

Final Thoughts

Quality Engineering is evolving. By embracing both Shift-Left and Shift-Right testing, organizations can deliver software that is not only fast and functional but also resilient and user-centric.

The future of quality is continuous. Are you ready to shift?