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AlgoViz — Interactive Algorithm Visualizer

An interactive learning platform for understanding algorithms and data structures through step-by-step visualizations.

AlgoViz is a web-based educational tool designed to make computer science concepts easier to understand. It helps students, developers, and computer science enthusiasts explore algorithm behavior through visual execution, interactive controls, and algorithmic complexity analysis.

Rather than relying only on theoretical explanations, AlgoViz aims to demonstrate how algorithms process data, make decisions, and reach their results.

Live Demo: https://algoviztool.vercel.app/

Repository: https://github.com/amanullahpy/algorithm_visualizer_tool

Table of Contents

Overview

Understanding algorithms is an essential part of computer science, but learning them can be challenging when explanations rely entirely on code, mathematical notation, or static diagrams.

AlgoViz addresses this challenge by presenting algorithm execution as a visual and interactive experience. Users can explore how data changes during execution, follow individual steps, and connect algorithm behavior with its underlying logic.

The platform is intended to support self-paced learning, classroom demonstrations, programming practice, and interview preparation.

Objectives

The primary objectives of AlgoViz are to:

  • Make fundamental algorithms easier to understand through visualization.
  • Help learners connect algorithm implementations with their theoretical explanations.
  • Demonstrate how algorithms manipulate and process data.
  • Support experimentation through interactive controls and user-defined inputs, where available.
  • Introduce time and space complexity in a practical learning context.
  • Provide an accessible starting point for exploring computer science fundamentals.

Features

Interactive Algorithm Visualization

Explore algorithm execution through visual representations of the underlying operations. Visual feedback helps users understand how data elements are compared, processed, or rearranged.

Step-by-Step Execution

Follow the execution process to better understand the sequence of operations performed by an algorithm. Step-based controls can help learners inspect individual stages instead of watching the entire process at once.

Execution Controls

Use the available playback controls to manage the visualization. Depending on the selected algorithm and implemented interface, these may include starting, pausing, stepping forward, resetting, or adjusting the execution speed.

Algorithm Information

Learn about the purpose and behavior of an algorithm alongside its visualization. Descriptive information can help users understand when an algorithm is useful and what trade-offs it involves.

Complexity Analysis

Explore the theoretical time and space complexity of supported algorithms. Complexity information provides context for understanding how performance changes as the input size increases.

Modern Web Interface

AlgoViz is designed as a browser-based application with an interface focused on learning, exploration, and usability.

Accessible Learning Experience

The project aims to make algorithm concepts easier to explore without requiring users to install a desktop application.

Feature availability depends on the current implementation. Update this section to match the controls and capabilities actually present in the application.

Supported Algorithms and Concepts

AlgoViz is intended to cover fundamental algorithmic concepts through interactive demonstrations.

The following categories provide a structure for documenting the algorithms supported by the application. Keep only the algorithms that are implemented and available in the live demo.

Sorting Algorithms

Sorting algorithms arrange data according to a specified order.

Potential examples include:

  • Bubble Sort
  • Selection Sort
  • Insertion Sort
  • Merge Sort
  • Quick Sort
  • Heap Sort

Searching Algorithms

Searching algorithms locate a target value within a collection of data.

Potential examples include:

  • Linear Search
  • Binary Search

Binary Search requires sorted input for its standard correctness guarantees.

Data Structures

Data structures organize information to support different operations and access patterns.

Potential examples include:

  • Arrays
  • Stacks
  • Queues
  • Linked Lists
  • Trees
  • Graphs

Additional Algorithmic Concepts

Depending on the implemented features, the platform may also demonstrate:

  • Recursion
  • Graph traversal
  • Tree traversal
  • Comparison-based operations
  • Input-dependent algorithm behavior

For each supported algorithm, the documentation should identify its purpose, input requirements, execution behavior, and expected complexity.

How It Works

The general learning workflow is:

  1. Select an algorithm: Choose a supported algorithm or concept from the interface.
  2. Provide input: Use the default data or enter custom input if the application supports it.
  3. Start the visualization: Run the algorithm and observe its execution.
  4. Inspect individual steps: Use the available controls to examine changes in the data.
  5. Review the result: Observe the final output and compare the execution with the algorithm's expected behavior.
  6. Study complexity: Review the relevant time and space complexity information, where available.

This workflow helps learners connect the algorithm's theoretical description with its actual execution.

Technology Stack

The project uses a modern JavaScript-based web development stack. Confirm the exact dependencies in package.json before finalizing this list.

Technology Purpose
JavaScript Application logic and interactive behavior
Next.js Web application framework
React Component-based user interface
CSS Styling and visual presentation
Tailwind CSS Utility-first styling, if configured
Vercel Hosting and deployment

The repository's current source files and dependency configuration should be treated as the authoritative reference for the technologies used.

Getting Started

Follow these instructions to run AlgoViz locally.

Prerequisites

You will need:

  • Node.js, using a version compatible with the project's dependencies
  • npm
  • Git
  • A modern web browser

Check your installed versions:

node --version
npm --version
git --version

Installation

1. Clone the repository

git clone https://github.com/amanullahpy/algorithm_visualizer_tool.git

2. Navigate to the project directory

cd algorithm_visualizer_tool

3. Install dependencies

npm install

4. Start the development server

npm run dev

5. Open the application

Navigate to:

http://localhost:3000

The development server should serve the application locally. Keep the terminal running while working on the project.

Production Build

To create a production build, run:

npm run build

If the build completes successfully, start the production server using:

npm run start

Open http://localhost:3000 to inspect the production version locally.

The production commands above assume that the corresponding scripts are configured in package.json.

Available Scripts

The following table describes common scripts for a Next.js application. Verify the actual script definitions before relying on these commands.

Command Purpose
npm run dev Starts the local development server
npm run build Builds the application for production
npm run start Starts the production server
npm run lint Runs configured linting checks, if available

If a command is not defined in the project, either add the appropriate script or remove it from this documentation.

Project Structure

The repository contains application source code, static assets, and configuration files. The following is a simplified representation of the known top-level structure.

algorithm_visualizer_tool/
├── public/                 # Static assets
├── src/                    # Application source code
├── .eslintrc.json          # ESLint configuration
├── .gitattributes          # Git attributes
├── .gitignore              # Files excluded from Git
├── jsconfig.json           # JavaScript project configuration
├── next.config.mjs         # Next.js configuration
├── package.json            # Project metadata and scripts
├── package-lock.json       # npm dependency lockfile
├── postcss.config.mjs      # PostCSS configuration
├── tailwind.config.js      # Tailwind CSS configuration
└── README.md               # Project documentation

The contents of src/ are not expanded here because the exact internal directory structure should be documented from the current source code.

Usage Guide

Selecting an Algorithm

Open the live application and choose one of the algorithms available in the interface. Review its description and any input requirements before starting the visualization.

Working with Input Data

If custom input is supported, provide values in the format required by the selected algorithm. For example, sorting algorithms typically operate on a collection of values, while searching algorithms require a collection and a target value.

Follow the application's input validation rules.

Controlling Execution

Use the available execution controls to observe the algorithm's behavior. If stepping, pausing, or speed adjustment is supported, experiment with these features to examine important operations.

Understanding Results

Compare the final output with the expected behavior of the algorithm. For sorting algorithms, verify that the output satisfies the requested ordering. For searching algorithms, verify that the reported result corresponds to the target value and input requirements.

Exploring Complexity

Use the complexity information to compare algorithms and understand the relationship between input size, execution time, and additional memory requirements.

Algorithm Complexity

Time complexity describes how an algorithm's running time grows with input size. Space complexity describes how its additional memory requirements grow.

The following examples are standard theoretical references for commonly studied algorithms, not a claim that all of them are implemented in AlgoViz.

Algorithm Best Time Average Time Worst Time
Bubble Sort O(n) with early-exit optimization O(n²) O(n²)
Selection Sort O(n²) O(n²) O(n²)
Insertion Sort O(n) O(n²) O(n²)
Merge Sort O(n log n) O(n log n) O(n log n)
Quick Sort O(n log n) O(n log n) O(n²)
Linear Search O(1) O(n) O(n)
Binary Search O(1) O(log n) O(log n)

Here, n represents the input size.

Complexity can depend on the input distribution, implementation details, optimizations, and assumptions. For example, the best-case complexity of Bubble Sort shown above assumes an implementation that detects when no swaps occur.

Space complexity should be documented separately for each implementation, especially when auxiliary arrays, recursion, or additional data structures are involved.

Testing and Quality Assurance

Testing is important for ensuring that visualizations accurately represent the behavior of their algorithms.

Recommended validation areas include:

  • Correctness of the algorithm's final output.
  • Correct ordering or search results for valid inputs.
  • Handling of empty collections and single-element inputs.
  • Handling of duplicate values and negative numbers where applicable.
  • Input validation and meaningful error feedback.
  • Correct behavior of play, pause, step, reset, and speed controls where implemented.
  • Responsive layout across desktop and mobile screens.
  • Successful production builds and absence of critical browser-console errors.

Run the project's configured lint and build checks before submitting changes.

These are recommended testing practices; automated test coverage should only be claimed after corresponding tests have been implemented and run.

Deployment

The application is available at:

https://algoviztool.vercel.app/

The repository is configured with a Vercel deployment. For future deployments, the typical workflow is:

  1. Connect the GitHub repository to a Vercel project.
  2. Configure the required framework and build settings.
  3. Configure environment variables if the application requires them.
  4. Deploy the selected branch.
  5. Verify the deployment URL and test important application features.

Production deployments should be checked after significant code changes to ensure that the application remains functional.

Refer to the official documentation for deployment guidance:

Security and Privacy

AlgoViz should follow standard web application security practices.

  • Do not commit passwords, API keys, tokens, or other private credentials.
  • Keep sensitive configuration out of source control.
  • Validate user input before processing it.
  • Avoid executing arbitrary user-supplied code.
  • Keep dependencies updated and review security advisories.
  • Avoid collecting or storing personal data unless it is required.
  • Document any external services that process user data.

Do not claim that the application stores no data, operates entirely offline, or provides a particular privacy guarantee unless this has been verified against the actual implementation.

Limitations

Algorithm visualizations simplify complex operations to make them easier to understand. They are educational aids rather than substitutes for studying algorithm correctness, implementation details, and formal complexity analysis.

Actual behavior may depend on:

  • The algorithms implemented in the current release.
  • The range and format of accepted inputs.
  • Visualization and browser performance.
  • The level of detail displayed during execution.
  • The availability of controls and complexity information.

Users should refer to the application itself for the algorithms and features currently supported.

Future Improvements

Potential directions for future development include:

  • Expand the collection of supported algorithms and data structures.
  • Add more detailed pseudocode and execution explanations.
  • Improve input customization and validation.
  • Provide clearer comparisons of algorithm performance.
  • Add educational examples and practice challenges.
  • Improve keyboard navigation and accessibility.
  • Add automated tests for algorithm correctness.
  • Establish continuous integration for linting and production builds.
  • Improve documentation with screenshots and usage examples.

This roadmap describes possible enhancements and should not be interpreted as a commitment to deliver every item.

Contributing

Contributions that improve correctness, usability, accessibility, documentation, or educational value are welcome.

Contribution Workflow

  1. Fork the repository.
  2. Create a branch for your change.
  3. Implement the improvement.
  4. Run the relevant checks locally.
  5. Confirm that existing functionality remains intact.
  6. Submit a pull request describing the change and its purpose.

For significant changes, open an issue first to discuss the proposed approach.

When contributing algorithm implementations, include appropriate input validation and tests for normal cases, edge cases, and expected output.

License

Check the repository for an existing LICENSE file before adding a license declaration.

If no license exists, the project should not be presented as freely reusable open-source software by default. Choose a license appropriate for your intended permissions and add the corresponding license file before claiming that the repository is licensed.

Author

Aman Ullah

AlgoViz aims to make algorithmic thinking more accessible through interactive exploration, clear explanations, and practical demonstrations of computer science fundamentals.