Enterprise AI Analysis
Leveraging LLMs for User Stories in AI Systems: UStAI Dataset
This paper explores the innovative use of Large Language Models (LLMs) to generate high-quality user stories for AI systems, addressing the scarcity of open-source requirements artifacts. Through an empirical evaluation of 1260 user stories generated from scholarly abstracts, we assess their quality, identify non-functional requirements (NFRs), and capture ethical principles. Our UStAI dataset provides a valuable resource for early requirements elicitation and research in AI system development.
Key Metrics & Immediate Impact
Our analysis uncovers critical data points showcasing the potential of LLM-driven user story generation for AI systems.
Total User Stories Generated
User Stories Evaluated
Research Abstracts Analyzed
Stories Emphasizing NFRs
Gemini Stories with Ethical Principles
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Methodology
Quality Comparison
Ethical Considerations
Practical Application
Enterprise Process Flow: User Story Generation with LLMs
Abstract Selection from Scholarly Papers
→
LLM Prompting for User Stories
→
User Story Generation
→
Quality Evaluation (QUS Framework)
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NFR & Ethical Principles Annotation
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UStAI Dataset Compilation
| Metric | Gemini 1.5-flash | Llama 3.1 70b | 01-mini |
|---|---|---|---|
| Syntactic Soundness | Good (Conjunctions common) | Good | ✓ Best (least issues) |
| Semantic Quality | ✓ Best (least issues) | More issues (e.g., non-user-centric roles) | Good |
| Pragmatic Quality | ✓ Best (least issues) | More issues (e.g., dependency, duplicates) | Good |
| Problem-Oriented Focus | ✓ Highest (83%) | Lower (solution-oriented common) | Lower (solution-oriented common) |
| Ethics Integration | ✓ Most Sensitive (33% stories) | Lower (15% stories) | Lower (20% stories) |
| Key NFR Emphasis | Usability | Testability | Interoperability |
33%
of Gemini's user stories incorporated ethical principles or considerations.
Gemini 1.5-Flash demonstrated the highest sensitivity to ethical requirements among the LLMs, mentioning or implying ethical principles in 33% of its generated user stories. This highlights its potential for early identification of crucial social responsibility aspects in AI system development, which is vital for building trustworthy AI.
AI System for Aggression Detection: Unforeseen Ethical User Story
In a study involving an AI system for Aggression Detection in Children with ADHD, Gemini 1.5-Flash generated a critical user story from an unexpected stakeholder perspective. Specifically, a user story emerged: 'As a data privacy advocate, I want assurances that any wearable device used for aggression detection collects data securely and protects children's privacy.' This was significant because the original abstract did not explicitly mention privacy as a feature or concern. This demonstrates Gemini's capability to identify and articulate important non-functional requirements and ethical considerations early in the requirements engineering process, even without direct prompting for such details, thereby enhancing the holistic design of AI systems.
Quantify Your AI Impact
Estimate the potential time savings and cost efficiencies your enterprise could achieve by implementing AI solutions derived from robust user story methodologies.
Advanced ROI Calculator
Estimated Annual Savings
Hours Reclaimed Annually
Your AI Implementation Roadmap
Leverage our insights to navigate the complexities of AI adoption, from initial requirements to sustained operational impact, ensuring a smooth and ethical deployment.
Phase 1: Requirements Elicitation & Prototyping
Harness LLM-generated user stories to rapidly define clear, multi-stakeholder requirements for your AI system, identifying key functional, non-functional, and ethical considerations early in the development cycle. Focus on creating early prototypes to validate core assumptions.
Phase 2: Model Development & Integration
Based on refined user stories, proceed with the development of core AI models and seamless integration into existing enterprise systems. Emphasize modular design and scalable architectures for future growth and adaptability.
Phase 3: Testing & Validation
Rigorously test the AI system against identified user stories, NFRs, and ethical principles. Implement comprehensive validation strategies to ensure accuracy, fairness, and reliability in real-world scenarios, addressing any ambiguities or conflicts.
Phase 4: Deployment & Continuous Improvement
Deploy the AI solution, monitor performance, and establish mechanisms for continuous feedback and iteration. User stories serve as a living document to guide ongoing enhancements, ensuring the AI system evolves with user needs and business objectives.
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