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About the Project
As QA programs scale, teams often hit an automation ceiling where script creation, locator maintenance, and failure triage slow down release cycles. Alio IT evolved beyond a traditional model by combining Katalon Studio and GitHub Copilot CLI to create an AI-First QA practice. This model helps teams move faster while improving traceability, quality, and operational consistency across Jira, QMetry, and Katalon workflows.
Objectives
Objective 1
Eliminate lag between Jira story creation and complete QA scenario coverage.
Objective 2
Accelerate automation scripting velocity across Katalon Studio assets.
Objective 3
Reduce manual maintenance overhead caused by frequent UI and locator changes.
Objective 4
Shorten failure triage cycles with AI-assisted root cause analysis.
Challenge
EdTech Client needed higher testing throughput without increasing manual effort. Traditional QA approaches required heavy manual drafting of test cases, slow script authoring, repetitive object repository work, and time-consuming log analysis during failures. This created operational drag as feature volume increased and made it harder to sustain release speed with confidence.
Services Provided
- AI-generated test scenarios and test cases in QMetry from Jira story cards
- AI-assisted import and normalization of legacy spreadsheet test cases into QMetry
- Copilot CLI generation of Katalon automation from Jira stories
- Copilot CLI generation of Katalon automation from QMetry test cases
- AI-assisted object repository creation in Katalon for complex applications
- AI-driven execution report analysis for fast root cause identification
- AI bug and execution cross-matching with locator update recommendations
- Software QA
- Engineering Operations
- Ongoing engagement