This is a submission for the Google AI Studio Multimodal Challenge
What I Built
I built a Test Case Generation Web Application that automatically transforms software requirements into structured test cases, ensuring full requirement traceability and detailed coverage analytics. The app addresses the common challenge faced by QA teams: manual test case design is slow, error-prone, and often leaves gaps in coverage.
With this application, QA teams can:
Upload Word, Excel, or PDF requirement documents, or input requirements manually.
Automatically generate industry-standard test cases mapped to a requirement traceability matrix.
Export results into flexible CSV formats with fields customized to the input type.
Visualize coverage analytics (requirement coverage, functional coverage, boundary value coverage, and test execution coverage) through an intuitive dashboard.
This creates a seamless workflow from requirement ingestion to actionable test suites, improving both speed and quality in the software testing lifecycle.
Demo
NextGen - AI Test Case Generator
How I Used Google AI Studio
I used Google AI Studiowith Gemini 2.5 Pro to power the multimodal requirement parsing and test generation engine:
Document parsing: Gemini models process text from PDFs, Word, and Excel simultaneously, extracting structured requirements and user stories.
Natural Language Understanding: Requirements are analyzed with Gemini’s language understanding to create meaningful test cases and boundary conditions.
Contextual mapping: AI aligns each generated test case to its originating requirement, forming a requirement traceability matrix.
Interactive refinement: Google AI Studio’s multimodal workspace allowed me to iterate quickly on prompts and test case generation logic with both text and document input.
Multimodal Features
The app leverages multimodal AI capabilities to create a richer experience:
Document-to-Test Automation: Users can upload mixed-format requirement documents (Word, Excel, PDF) and have them analyzed in one unified step.
Smart Coverage Analytics: Automatically calculates requirement coverage, functional coverage, and boundary value coverage based on parsed inputs.
Visual Dashboards: Converts AI outputs into interactive visualizations for traceability and execution metrics, helping QA teams identify gaps instantly.
Human-in-the-loop Editing: Users can review, edit, and fine-tune AI-generated test cases directly in the app before exporting.
This multimodal integration ensures QA teams no longer need separate tools for parsing, mapping, generation, and reporting—everything is streamlined into one AI-powered workflow.
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