Real Talk from an AI Expert – Why You Need a Dedicated AI-driven Software QA (SQA) Framework

AI-driven SQA is transforming testing lifecycle. This shift is about faster and smarter testing with up to 80% less manual efforts and 40% faster execution. The future of AI in QA lies in frameworks that adapt to context, risk, system changes, and can learn over time.

Dedicated AI-driven QA frameworks are different from ChatGPTs or Copilots. Latter can generate code or test snippets, but they lack context, application workflow understanding, and visibility about testing goals.

Dedicated AI-driven QA frameworks are customizable to your requirements, integrate seamlessly into CI/CD pipelines, adapt to changing requirements over time and can use past data and context to improve test code snippets.

We spoke with Vishwanath Pula, AVP – Solution Architect & Customer Service, and the mind behind Enhops’ AI-driven Testing Framework. He outlined why teams need dedicated AI-driven SQA frameworks to make the most of AI in QA.

Why AI Tools Like ChatGPT and Copilot Fall Short

A lot of people ask, why not just use ChatGPT or Copilot for test case generation? They’re fast. You paste in a requirement, prompt it, and you get test cases right away.

The answer is – ChatGPT and Copilot are powerful generators, but they are blind to your context. They don’t know your domain rules, your requirements, your evolving codebase. They don’t remember what changed last sprint.

These tools generate disconnected outputs and lack the contextual awareness needed for enterprise QA. As they are not integrated in your pipeline, they don’t have access to metrics like your historical failure rate, test stability scores, or recurring defects, so, they fail to adjust as the system evolves.

Moreover, AI in QA has immense potential—and using it solely for blind test case generation is a missed opportunity

What is Dedicated AI-driven SQA Framework?

An AI-driven SQA Framework is designed to accelerate manual testing tasks and free up testers to focus on high-value, strategic work.  It intelligently understands your system context, adapts to changes, and integrates with your dev and QA workflows.

Enhops has helped numerous clients streamline QA and identified a clear need: an AI-driven SQA framework adaptable to each business’s unique needs. So, we have built one.

Enhops AI-driven SQA Framework Features

At Enhops, we’ve built an AI-driven SQA Framework that supports all kind of greenfield (new projects), brownfield (enhancing existing systems), and Bluefield applications (a hybrid approach—modernizing parts of legacy systems while building new capabilities). Some of its features are:

1. AI Agentic Framework – Customised agentic workflows for structured test case management, risk-based prioritization, test optimization, test case data handling, and execution control across the lifecycle.
2. AI-Powered Test Case Management – Enables structured test case generation and categorization, optimizes both existing and new test suites. Streamlines test case maintenance with synthetic data generation and intelligent suite-level analysis.
3. Automation and Integration – Seamless integration with CI/CD pipelines, Jira, and Azure DevOps to support continuous testing, faster feedback loops, and smooth releases.
4. Advanced Outputs – Supports integration with BDD workflows through automation scripts in Gherkin formats and customizable code templates for consistent test documentation.
5. Analysis and Reporting – Real-time dashboards with test distribution, execution status, priority breakdown, confidence scores, and analytics for better QA insights.
6. Self-Healing AI – Automatically detects changes in the UI or DOM and auto correct test paths using intelligent selectors and maintains logs to reduce flaky tests and maintenance effort.
7. Knowledge Management – Centralized repository for storing test history, traceability data, reusable artifacts, and learned behavior, for smarter regression testing.

6 Key Advantages of AI-driven SQA Framework

A dedicated AI-driven SQA Framework offers six key advantages that generic tools can’t match:

• Contextual Awareness – Dedicated Frameworks understand your requirements (SRS), domain rules, past defects, and evolving code changes, ensuring tests stay relevant and accurate.
• Test Intelligence – They prioritize and generate tests only where the code or requirements have changed, optimizing coverage and effort for maximum ROI.
• Continuous Learning – These systems adapt based on test results and user feedback from each run, improving their effectiveness over time.
• Autonomous Agents – Test generation, execution, and maintenance are orchestrated without manual triggers, enabling end-to-end automation.
• System Integration – Tight integration with your repositories, CI/CD pipelines, issue trackers, and test management tools ensures full traceability, explainability, and audit trails.
• Human-Readable Output – Every AI-generated test is linked back to a requirement and includes clear, human-readable rationale for full transparency.

That is why you need a dedicated AI-driven SQA Framework. Not just a generator, but a system that understands your ecosystem, learns from your data, and evolves with your code. At Enhops, we’ve built a framework that does exactly that. Our AI-driven testing framework connects seamlessly across codebases, CI/CD pipelines, issue trackers, and test management tools to deliver smarter, more scalable QA.

Enhops Helps You Test Smarter, Release Faster

At Enhops, we offer AI-driven testing services that Our customizable framework improves test coverage, reduces manual effort, and adapts quickly to change. By applying AI to areas like test case generation, self-healing automation, impact analysis, and intelligent defect detection, we enable smarter testing without disrupting your current setup.

Whether you’re scaling automation, enhancing test efficiency, or modernizing your QA strategy, we work with you step by step to deliver faster, high-quality releases.