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AutomataLab Project Status and Roadmap

Current Status

AutomataLab is currently in active development, successfully functioning as a cross-platform desktop application powered by Tauri and React. The core engine is capable of simulating finite state machines and pushdown automata with a highly interactive, node-based visual interface.

Implemented Features

• Supported Machine Types:
  • Deterministic Finite Automata (DFA)
  • Non-Deterministic Finite Automata (NFA)
  • Epsilon-NFA (e-NFA)
  • Deterministic Pushdown Automata (DPDA)
  • Non-Deterministic Pushdown Automata (NPDA)
• Visual Editor:
  • Drag-and-drop canvas for placing states and mapping transitions.
  • Live validation of the defined alphabet and state connections.
  • Live stack panel with push/pop animations for PDAs.
  • Status-coloured computation-tree viewer for exploring all branches of a non-deterministic run.
• Simulation Engine:
  • Step-by-step execution to inspect state and stack changes.
  • Continuous execution with customizable playback speeds.
  • Full support for non-deterministic branching computations.
• Application Features:
  • Multi-tab interface to work on multiple automata simultaneously.
  • Native OS file dialogs for saving and loading projects as .autolab.json configurations.
  • Cross-platform builds available for Windows, macOS, and Linux.
  • Integrated over-the-air update system.

Project Roadmap and Future Direction

The primary goal of AutomataLab is to evolve from a finite automata and pushdown automata simulator into a comprehensive visual learning environment for all levels of computational theory. The next major milestones focus on expanding the types of computational models supported and enhancing the analytical tools available to users.

Phase 1: Advanced Computational Models

• Turing Machines (TM):
  • Implement an infinite tape memory model.
  • Introduce UI components for tape visualization and read/write head movement.

Phase 2: Analytical and Educational Tools

• Machine Conversion: Implement automated tools to convert an NFA to a DFA.
• State Minimization: Implement algorithms to optimize and minimize DFA states.
• Regex Integration: Allow users to generate a DFA/NFA directly from a Regular Expression, and vice versa.

Phase 3: Export and Accessibility

• Visual Exports: Add functionality to export the current canvas as a PNG, JPG, or SVG for use in assignments and presentations.
• Web Version Parity: Ensure that the core simulation engine remains completely decoupled from Tauri, allowing for a fully functional web-browser version of AutomataLab in the future.