🧠 Tutorial: Using Visual Paradigm’s AI-Powered Textual Analysis for Software Design

From messy problem statements to clean, actionable class diagrams—in minutes.

🎯 What Is AI-Powered Textual Analysis?

Visual Paradigm’s AI-Powered Textual Analysis is an intelligent modeling assistant that helps product managers, software architects, and developers translate unstructured natural language (e.g., user stories, requirements, or system descriptions) into a structured domain model — specifically, a UML Class Diagram.

Instead of manually identifying entities, attributes, and relationships, the AI parses the text, extracts relevant design elements, and proposes a visual model you can refine.

🔍 Core Idea: Turn narrative → nouns → classes → relationships → diagram — automatically.

✅ Key Advantages

🛠️ Step-by-Step Tutorial (With Real Examples)

Let’s walk through three progressively complex examples, from simple to enterprise-grade.

📘 Example 1: Library Management System (Starter)

📝 Step 1: Provide Problem Description

• Go to Tools > Apps > Textual Analysis > Start Now
• Enter: Library Management System
• Click [Generate Problem Description]

👉 AI generates:

“A library management system allows librarians to manage books, members, and loans. Members can search for books, borrow up to 5 items at a time, and return them. Overdue books incur fines. Each book has a title, author, ISBN, and availability status. Librarians can add/remove books and view loan history.”

✅ Edit if needed — e.g., add: “Supports digital e-books and reservation queues.”

👥 Step 2: Identify Candidate Classes

Click [Identify Candidate Classes]

🔁 Also see: “Nouns Not Qualified” (e.g., status, history → too vague or attribute-like).

✅ Accept all, or remove Librarian if roles are handled via permissions (e.g., using User + role flag).

📋 Step 3: Identify Class Details

Click [Identify Class Details]

Sample output for Book:

• Attributes:
  isbn: String
title: String
author: String
isAvailable: Boolean
format: Enum {Physical, Digital}
• Operations:
  checkAvailability(): Boolean
markAsBorrowed()
markAsReturned()

For Loan:

• Attributes:
  loanDate: Date
dueDate: Date
returnDate: Date?
• Operations:
  calculateOverdueDays(): Int
applyFine()

💡 Pro Tip: Rename isAvailable → status: BookStatus (enum: Available, Borrowed, Reserved) for extensibility.

🔗 Step 4: Identify Class Relationships

Click [Identify Class Relationships]

⚠️ Watch out: AI may miss aggregation vs composition. Edit manually if Loan should reference (not own) Book.

🖼️ Step 5: Generate Diagram

Click [Generate Diagram] → A full UML Class Diagram appears!

✅ Then click [Open in Visual Paradigm Online] to:

• Rearrange layout
• Add stereotypes («entity», «boundary»)
• Link to use cases or sequence diagrams
• Export as PNG, PDF, or generate Java/Python stubs

🛒 Example 2: E-Commerce Shopping Cart (Intermediate)

Input Prompt:

“Online store where users browse products, add items to cart, apply promo codes, checkout with credit card or PayPal, and track orders. Admins manage inventory and view sales reports.”

AI-Identified Classes:

• User, Product, ShoppingCart, CartItem, Order, Payment, PromoCode, Inventory, Admin

Notable Relationships:

• ShoppingCart ◇—— CartItem (aggregation; cart has items, but items aren’t destroyed with cart)
• Order ◆—— Payment (composition; payment is part of order lifecycle)
• PromoCode —— Order (0…1 → 1; optional at checkout)

Insight Gained:

AI suggests CartItem as separate from Product — good! Because:

• CartItem has quantity, addedAt, and snapshot of price (to handle price changes).
• Product has currentPrice, stockLevel.

➡️ Prevents common modeling mistake: conflating catalog item with cart line item.

🏥 Example 3: Hospital Appointment System (Advanced)

Input Prompt (edited for realism):

“Patients schedule appointments with doctors. Each appointment has a date/time, type (e.g., consultation, follow-up), and status (scheduled, completed, canceled). Doctors have specialties and work schedules. The system sends reminders 24h prior. Nurses can check patients in. Lab results are attached post-visit.”

AI Highlights:

Smart Relationship:

• Appointment ◆—— LabResult (1 → 0…*)
  → Enforces: Results only exist for completed appointments.

Hidden Gem:

AI flags "type" and "status" in appointment → suggests enums:

enum AppointmentType { CONSULTATION, FOLLOW_UP, VACCINATION }
enum AppointmentStatus { SCHEDULED, CHECKED_IN, COMPLETED, CANCELED }

✅ Developer saves time defining domain enums + validation logic.

🚀 Pro Tips for Maximizing Value

⚠️ Limitations & How to Mitigate

📊 When to Use It (Best Fit Scenarios)

🌐 Next Steps: Beyond the Diagram

Your AI-generated class diagram is just the beginning. In Visual Paradigm, you can:

1. Generate Database Schema → ERD → SQL DDL
2. Derive Sequence Diagrams from operations (e.g., Order.checkout())
3. Link to Requirements (e.g., tie applyPromoCode() to BRD section 4.2)
4. Simulate with VP Model Simulation
5. Publish as Web Portal for stakeholder review

📬 Final Thought

“The AI doesn’t replace the designer — it replaces the tedium.”
Use Textual Analysis to get 80% of the model right in 20% of the time, then invest your expertise in the critical 20%: edge cases, scalability, and domain nuance.

📎 Ready to Try?
→ Launch: Visual Paradigm Online
→ App: Tools > Apps > Textual Analysis

Let me know if you’d like:

• A downloadable cheat sheet (PDF)
• Template prompts for fintech, SaaS, IoT, or healthcare domains
• Comparison with manual CRC/Domain Modeling

Happy modeling! 🧩