Demo Projects

Hands-on demonstrations that bring software effort estimation concepts to life through practical implementations.

Table of contents
  1. Overview
  2. Available Demos
    1. Module 2: Analogy-Based Estimation Demo
    2. Module 3: Project Estimation & Gantt Chart App
  3. Getting Started
    1. Prerequisites
    2. Setup Instructions
      1. Analogy Demo Setup
      2. ProjectEstimator App Setup
      3. Development in VS Code
  4. Demo Progression
    1. Learning Path
  5. Extension Opportunities
    1. Analogy Demo Extensions
    2. ProjectEstimator Extensions

Overview

This section contains interactive demos that complement the theoretical concepts covered in the course modules. Each demo is designed to provide practical, hands-on experience with different estimation techniques and tools.

Available Demos

Module 2: Analogy-Based Estimation Demo

Technology: Python, Jupyter Notebook, scikit-learn
Location: demos/AnalogyDemo/
Complexity: Beginner

Interactive Jupyter notebook demonstrating analogy-based estimation using machine learning similarity metrics. Learn how to:

  • Use historical project data for estimation
  • Calculate project similarity using Euclidean distance
  • Apply k-nearest neighbors for estimation
  • Experiment with different project parameters

Key Learning Outcomes:

  • Understanding similarity-based estimation
  • Hands-on experience with Python data analysis
  • Practical application of machine learning concepts
  • Interactive experimentation with estimation parameters

Open Analogy Demo →

Module 3: Project Estimation & Gantt Chart App

Technology: ASP.NET Core, C#, Entity Framework, Mermaid.js
Location: demos/ProjectEstimator/
Complexity: Intermediate

Full-featured web application demonstrating comprehensive project management and estimation techniques. Features include:

  • Three-point estimation with PERT calculations
  • Task dependency management
  • Automated Gantt chart generation
  • Critical path analysis
  • Interactive project timeline visualization

Key Learning Outcomes:

  • Three-point estimation implementation
  • Task dependency modeling
  • Critical path analysis
  • Web-based project management tools
  • Integration of estimation with project scheduling

Open ProjectEstimator App →

Getting Started

Prerequisites

Choose the demo that matches your learning goals and technical background:

For Python/Data Science learners (Analogy Demo):

  • Python 3.7+ installed
  • Jupyter Notebook or VS Code with Python extension
  • Basic familiarity with Python and pandas

For Web Development learners (ProjectEstimator):

  • .NET 8.0 SDK
  • Visual Studio Code or Visual Studio
  • Basic understanding of web development concepts

Setup Instructions

Analogy Demo Setup

  1. Navigate to demos/AnalogyDemo/
  2. Open analogy_demo.ipynb in Jupyter Notebook or VS Code
  3. Run the package installation cell: !pip install scikit-learn
  4. Execute cells sequentially to see the demo in action

ProjectEstimator App Setup

  1. Navigate to Demo Directory 
    cd demos/ProjectEstimator
  2. Restore NuGet Packages 
    dotnet restore
  3. Create Database 
    dotnet ef database update
  4. Run Application 
    dotnet run
  5. Open in Browser Navigate to http://localhost:5000

Development in VS Code

  1. Open the demos/ProjectEstimator folder in VS Code
  2. Press F5 to start debugging
  3. The application will open in your default browser
  4. Use breakpoints for debugging the C# code

Demo Progression

The demos are designed to build upon each other, progressing from simple estimation concepts to more complex project management implementations:

Learning Path

  1. Start with Analogy Demo (Module 2) - Learn fundamental estimation concepts
  2. Progress to ProjectEstimator (Module 3) - Apply concepts in a real application
  3. Experiment and Extend - Use the provided exercises to explore further

Extension Opportunities

Both demos provide opportunities for further exploration:

Analogy Demo Extensions

  • Add more sophisticated similarity metrics
  • Implement cross-validation for model evaluation
  • Experiment with different machine learning algorithms
  • Create visualizations of project similarity patterns

ProjectEstimator Extensions

  • Integrate Monte Carlo simulation
  • Add resource management features
  • Implement team collaboration tools
  • Create advanced reporting and analytics

These demos provide practical, hands-on experience that complements the theoretical concepts covered in the course modules. Each demo is designed to be both educational and extensible, allowing you to explore estimation concepts in depth.

Table of contents