Case Study
Leading Digital Agreement Platform Success Story: Achieves 70% AI Accuracy in Test Case Generation
Download Case StudyIndustry: Enterprise Software / Digital Agreement Management
Key Technologies / Platforms: Crew AI, Azure OpenAI, Azure AI Search, ChromaDB, FastAPI, Next.js, AWS, Jira
API, Zephyr Scale API, Google Drive API, Retrieval-Augmented Generation (RAG)
About the Client
The client is a leading global SaaS platform provider delivering cloud-based solutions that help organizations streamline digital agreement processes, improve collaboration, and automate business workflows.
With a strong focus on innovation and customer experience, the organization continuously enhances its software platforms while maintaining high standards of security, reliability, and quality across enterprise applications.
Business Challenges
- Slow and Manual Test Case Creation: QA teams spent significant time analyzing requirements, understanding business scenarios, and manually creating test cases, resulting in longer testing cycles and delayed releases.
- Inconsistent Test Coverage and Quality: Different QA teams followed varying approaches for test design, creating inconsistencies in test quality, coverage, and validation standards.
- Limited Identification of Edge Cases: Manual test creation made it challenging to consistently identify complex scenarios, negative test cases, and potential failure conditions.
- Lack of Requirement-to-Test Traceability: Maintaining clear relationships between user stories, requirements, and test cases required significant manual effort and impacted visibility.
- Delayed Review and Approval Cycles: Manual communication and review processes slowed stakeholder feedback, validation, and test execution readiness.
- Need for Scalable QA Operations: Growing engineering demands required a standardized approach to testing that could support multiple teams while maintaining quality and speed.
Business Requirements
- Automate test case generation from user stories, requirements documents, product documentation, and external sources.
- Reduce manual effort involved in test design and improve QA team productivity.
- Enable integration with Jira to automatically retrieve user stories, project information, and requirements.
- Support multiple input formats, including documents, URLs, and knowledge repositories.
- Leverage historical test data and validated scenarios through RAG-based knowledge retrieval for context-aware test generation.
- Generate functional, negative, and edge-case test scenarios automatically.
- Enable QA teams and stakeholders to review, modify, approve, and manage AI-generated test cases.
- Integrate with test management platforms such as Zephyr Scale for seamless test execution workflows.
Our Approach and Solutions
- AI-Powered Requirement Analysis and Test Design: Jade Global developed an AI-driven Quality Agent that automatically analyzes user stories, business requirements, product documents, and external knowledge sources to identify testing scenarios and acceptance criteria. The platform converts complex requirements into structured test cases, reducing manual analysis effort.
- Generative AI-Based Test Case Creation: Using Azure OpenAI and Crew AI-powered multi-agent orchestration, the solution automatically generates detailed functional, positive, negative, and edge-case test scenarios. This enables QA teams to achieve broader coverage while reducing time spent on repetitive test creation activities.
- RAG-Based Knowledge Intelligence: The platform uses Retrieval-Augmented Generation (RAG) to leverage historical test cases, validated scenarios, business rules, and project-specific knowledge. This ensures generated test cases are context-aware, aligned with existing testing standards, and relevant to business requirements.
- Jira and Zephyr Scale Integration: The solution integrates with Jira to retrieve user stories and project details while enabling seamless publishing of generated test cases into Zephyr Scale. This creates end-to-end traceability from requirements through test execution.
Impact and Business Benefits
- Reduced test case creation time from 3 days to just minutes, accelerating QA cycles and improving release velocity.
- Saved more than 1,000 engineering hours across 25+ QA professionals by eliminating repetitive manual test creation activities.
- Generated 10–20 test cases per user story by analyzing requirements, documents, and historical testing knowledge.
- Successfully scaled adoption across 26 active users, generating approximately 1,500–1,800 test cases.
- Achieved approximately 70% first-pass AI accuracy for test scenario coverage and detailed test case generation, allowing QA teams to focus on validation and refinement.
- Improved requirement-to-test traceability through automated linking of user stories, requirements, and test cases.
- Enhanced QA consistency by standardizing testing methodologies across engineering teams.
- Enabled faster software delivery cycles through AI-driven test automation and intelligent workflow orchestration.
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