App Development

AI Applied to QA at GooApps®: From Generating Fast Tests to Generating Reliable Tests

At GooApps®, integrating artificial intelligence into Quality Assurance (QA) is not about replacing testers, but about enhancing their analytical capabilities. We have moved from isolated prompt usage to a structured, automated workflow that classifies tasks, analyzes repository code, and generates test cases compatible with Jira Xray. This approach ensures that AI is not a “black box,” but a coverage and risk-detection tool under strict human supervision.

The problem: why speed is not the only goal

In software development, test case creation is often costly and repetitive. Impulsive use of AI (basic chatbots) promises speed, but frequently delivers hallucinations or generic tests that fail to respect business logic.

At GooApps®, we have defined that the real value of AI in QA lies in judgment and control. Our goal is not for AI to “do the testing,” but to use it to detect edge cases and scenarios that a human might overlook due to fatigue or time constraints.

Comparison: the evolution of testing at GooApps®

From chaos to system: a structured automation workflow

One of the main risks of using AI is inconsistency. To avoid this, we designed a system that does not rely on “asking better questions,” but on a software engineering process.

1. Intelligent task classification

Before generating anything, the system analyzes the documentation and automatically classifies the type of work. An API is not tested the same way as a mobile app.

• The system determines: Is it an API? A native app? A back-office system?
• Impact: prevents AI from applying web logic to a mobile application—a common mistake in generic models.

2. Deep repository analysis (GitHub)

This is the differentiating step. The AI does not only read functional requirements (Jira/Confluence), but also accesses the relevant code in the repository.

• Discrepancy detection: identifies whether what the documentation states matches what is actually implemented in the code.
• Technical risk identification: suggests tests based on fragile points in the code, not just on the “user story.”

3. Generation adapted to the product type

The system applies testing strategies specific to each vertical:

• For APIs: generates JSON structure tests, HTTP status codes, error validation, and test cases ready for Postman.
• For mobile apps: focuses on permissions, connectivity states (offline/online), native navigation flows, and usability across different devices.
• For web/CRM: validates inputs, complex forms, and end-user workflows.

Technical integration: test cases ready for Jira Xray

The workflow does not end with text. We transform the generated knowledge into direct digital assets. The system produces CSV files specifically formatted for import into Xray (our testing management tool in Jira).

The full cycle:

1. Input: Functional documentation + code.
2. Process: Analysis and scenario generation (happy path + negative cases).
3. Output: CSV file with mapped fields (Action, Expected Result, Pre-conditions).
4. Result: Test tasks automatically created and linked to the original Jira ticket.

This eliminates manual copy-paste and ensures full traceability.

The role of QA: mandatory human validation

Even with an automated workflow, responsibility remains human. We apply the “human-in-the-loop” principle: no AI output is considered valid without review.

What does QA do when AI fails?

If the model generates irrelevant cases or hallucinates, we do not “patch” the final output.

1. We adjust the workflow or system prompt.
2. We refine the provided context.
3. We narrow the scope for the next execution.

This turns error into systemic learning, improving the tool for the entire team.

Conclusion: AI to raise the bar, not to eliminate the expert

The biggest lesson from integrating AI into our QA processes at GooApps® has not been technological, but methodological: AI only delivers real value when its work is explainable, verifiable, and improvable.

We do not use automation to replace human judgment, but to elevate it. By freeing our QA Engineers from repetitive writing of basic test cases (happy paths), we allow them to evolve into quality strategists. Their focus is now where machines fall short:

• Quality strategy: designing robust test plans for complex, distributed architectures.
• User experience (UX): validating that technology feels human, intuitive, and accessible.
• Security and compliance: ensuring data integrity in critical environments—especially in our HealthTech projects, where errors are not an option.

This approach not only consolidates quality in the present, but also prepares teams for the next level of maturity in AI-assisted development, moving toward AI agent systems with defined roles, human oversight, and persistent context.

In 2026, speed is a commodity, but trust is the true differentiator. AI accelerates execution, but it is the human team at GooApps® that ensures excellence.