SAP QA Copilot – AI-driven Testing Assistant

Inspiration

During my experience in software testing and quality assurance, I noticed how time-consuming and error-prone manual testing of SAP business processes can be. I wanted to create a tool that leverages AI to assist QA engineers, making it faster and more accurate to detect issues, generate tests, and produce actionable reports.

What it does

SAP QA Copilot automatically: • Analyzes SAP business process logs for anomalies and errors. • Generates regression test suggestions based on detected issues. • Produces human-readable QA reports in natural language. • Provides a seamless workflow for QA engineers to review and act on findings.

It essentially acts as an AI-powered assistant that reduces manual effort, speeds up testing cycles, and improves process reliability.

How we built it

• Backend: Python (FastAPI) for processing logs and integrating AI services. • AI Analysis: SAP AI Core to detect anomalies and predict errors. • Natural Language Reports & Test Generation: Generative AI Hub. • Optional Frontend: SAP UI5 / SAP Build Apps for easy log uploads and report viewing. • Storage: SAP Object Store / Data Lake for logs and generated reports. • DevOps: Docker for containerization, GitHub for version control.

The system workflow is: log upload → AI Core analysis → Generative AI Hub → report + test generation → QA revie

Challenges we ran into

• Structuring and cleaning SAP log data for AI processing. • Designing effective prompts for Generative AI to produce clear, actionable reports. • Integrating multiple SAP services seamlessly in a single workflow. • Optimizing AI performance to handle large enterprise datasets efficiently.

Accomplishments that we're proud of

• Developed a working prototype that analyzes SAP logs, detects anomalies, and generates test cases automatically. • Created natural language QA reports, making results understandable to non-technical users. • Demonstrated that AI can significantly reduce manual QA effort in enterprise envir

What we learned

• How to combine AI Core and Generative AI Hub effectively for enterprise workflows. • How to engineer prompts and structure data for reliable AI outputs. • Practical insights into AI-powered QA processes and integration with SAP BTP. • The importance of clear reporting and user-friendly interfaces for adoption in business environments.

What's next for SAP QA Copilot – AI-driven Testing Assistant

• Expand support to more SAP modules and process types. • Improve AI accuracy with continuous learning from QA feedback. • Integrate with automated testing frameworks to execute generated regression tests. • Add a dashboard for QA managers to visualize trends, issues, and test coverage.

Built With

• fastapi
• javascript
• python