At GooApps®, applying AI to QA has never been about speed. It has always been about judgment and control. As a QA professional, my goal is not to have AI “do the testing for me,” but to use it to improve coverage, detect risks earlier, and standardize test case quality without losing traceability or accountability.
The human problem we aim to solve is clear: test case generation is often costly, repetitive, and highly dependent on context. This leads to inconsistencies across projects and wasted time on low-value tasks. AI can help—but only if it is integrated into a proven, validated, and explainable workflow.
Over the past year, I have continuously used AI across multiple projects to:
What started as occasional prompt usage evolved into a fully automated workflow that allows us to:
AI does not operate as a black box here. It functions as a component within a controlled system.
| Initial AI Approach | Structured GooApps® Approach |
|---|---|
| Isolated prompt | Replicable automated workflow |
| Manually generated cases | Pre-classification by product type |
| Inconsistent format | X-Ray-compatible standardization |
| Executor-dependent | Traceable and auditable process |
| Informal review | Mandatory human validation |
The shift was not technological. It was methodological.
The primary inputs are functional and technical documentation. This introduces clear risks when applying AI to testing:
For this reason, our core principle is simple and non-negotiable: no AI-generated output is considered valid without human review.
Every output goes through:
AI accelerates the process. Accountability always remains with QA.
Control over the workflow always remains with the user. AI proposes, but it does not decide.
We can assert that results are explainable because we know:
When the model fails—irrelevant cases, incorrect assumptions, noise—the system is not patched superficially. The workflow is adjusted, prompts are refined, or scope is limited. That learning becomes part of the process and improves future executions.
One of the biggest risks of applying AI in QA is remaining at the level of isolated solutions: loose prompts and inconsistent outputs. To avoid this, the focus was on designing a structured workflow, not on “asking AI better questions.”
The workflow always starts from a specific task and automates test creation from the beginning, following GooApps® internal standards. AI does not replace QA judgment; it executes defined steps within a clear process.
Before generating any test case, the system automatically classifies the task type (API, App, CRM, Backoffice, or WebApp) by analyzing documentation and functional context.
This step is critical. It makes no sense to apply the same testing logic to an API and a mobile application.
Classification prevents one of the most common AI-in-QA errors: generating generic test cases that ignore product nature.
When the task is API-related, the workflow generates:
The goal is not only to validate correct responses, but also to cover structure, HTTP status codes, invalid inputs, and consistency with functional documentation.
In these environments, AI works with extended, structured prompts. Test cases follow a consistent order:
AI shifts from being a text generator to becoming an assistant aligned with internal QA standards.
For mobile applications, priority is given to:
The objective is not to replicate manual testing, but to expand coverage where time constraints typically limit us.
One differentiating step in the workflow is repository code analysis related to the task. AI receives both documentation and relevant source code.
This allows us to:
AI does not replace technical judgment. It reduces exclusive dependency on incomplete documentation.
The final step transforms generated knowledge into executable test cases ready for X-Ray import, in Jira-compatible CSV format.
The cycle is complete: Documentation → analysis → generation → validation → execution
No manual copy-paste.
No later reinterpretation.
Direct traceability between task, test case, and execution.
The main learning has not been technical, but methodological: AI in QA only creates value when the process is explainable, validatable, and improvable.
If we do not understand why a test case exists, we should not execute it.
At GooApps®, using AI in QA does not mean losing control. It forces us to define our quality criteria more precisely. And that is what truly elevates testing standards.
No. AI can accelerate creation and expand coverage, but scope interpretation, validation, and accountability remain human responsibilities.
False confidence in coverage. Without human review, AI may generate structurally correct but conceptually incorrect test cases.
Because each type (API, App, CRM, WebApp) requires different testing logic. Without classification, test cases tend to become generic and ineffective.
It helps detect mismatches between documentation and implementation and enables test case generation based on real technical risks.
AI only delivers value within a structured, traceable, and reviewed process. Without methodology, it simply generates technical debt.
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