AI-Powered Scenario-to-Test Case Generation for PLM Workflows
Project Details
Duration
03 Months
Team Size
8 specialists
Industry
PLM Software
Technologies
Azure AI Search
Azure App Service
Microsoft Entra ID
Azure Blob Storage
OpenAI
LangSmith
Ragas
Python
Streamlit
Flask
Project Overview
In an engineering workflow integration platform for a Product Lifecycle Management (PLM) software, the process of converting high-level business scenarios into detailed technical test cases was traditionally manual, time-consuming, and prone to inconsistency. To address this, an AI-powered solution was developed using a Retrieval-Augmented Generation (RAG) approach that automatically generates test cases from business-level scenario descriptions. Leveraging historical scenario–test case mappings and exhaustive PLM documentation, the system integrates Azure AI Search, Azure Blob Storage, and Azure Web Services to deliver a streamlined and intelligent test case generation process through a user-friendly Streamlit interface.
Challenges & Solutions
Challenges
Manual dependency
Generating test cases from business scenarios required extensive manual effort and domain expertise.
Data complexity
The PLM documentation and test case data were large, unstructured, and required careful preprocessing and tagging for relevance.
Duplicate and redundant outputs
The model initially generated repetitive or overlapping test steps, affecting quality.
Performance bottlenecks
Retrieval latency from large vector datasets slowed down the generation process.
User experience
Long-running generation tasks lacked feedback or visibility, leading to poor user interaction and trust.
Solutions
RAG-based architecture
Combined Azure AI Search with LLMs to ground the generation process in accurate, context-aware PLM documentation and historical data.
Data curation and tagging
Meticulously organized and annotated documents and test cases to ensure high-quality retrieval and contextual relevance.
Iterative summarization technique
Introduced to eliminate duplicate steps and improve output coherence.
Parallelized retrieval
Optimized performance by running concurrent retrieval processes, significantly reducing response time.
Interactive UI and feedback loop
Developed a Streamlit interface enabling users to upload Excel-based inputs, track progress, and provide targeted feedback on generated steps for regeneration and continuous improvement.
Observability and evaluation
used LangSmith for tracing system output and debugging while used Ragas for evaluation of system performance ensuring measurable reliability and accuracy.
Results & Impact
70–80% reduction in manual effort
For test case authoring across common PLM workflows.
Enhanced accuracy and consistency
In generated test cases through context-grounded retrieval and fine-tuned data tagging.
Improved system performance
Via parallel retrieval and optimized query execution.
User satisfaction and adaptability
Increased through transparent progress updates, feedback-driven refinement, and explainable outputs.
Robust evaluation and monitoring
Achieved using Ragas for generation quality assessment and Langsmith for observability and traceability.
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