Parsers

This repository contains two educational parser implementations commonly studied in automata theory and compiler design courses:

1. A Regular Language Parser based on Deterministic Finite Automata (DFA) using Python.
2. A Shift-Reduce Parser for simple grammars, implemented in C++.

Each parser includes test data and configuration files for demonstration purposes.

---

📁 Directory Structure

🔹 1. Regular Language Parser/

This directory implements a DFA-based simulator that reads a configuration file describing the automaton and evaluates whether a given input string is accepted.

📄 Contents

File Name	Description
automata_demo.py	Basic DFA simulator using hardcoded transition functions.
automata_demo_extended.py	Extended DFA simulator that reads transitions, initial state, and final states from an external file (model2.txt).
model.txt/model2.txt	Primary test files used with automata_demo_extended.py. Contains full DFA config (transitions, initial state, final states).

🔹 2. Shift_Reduce_Parser/

This directory contains a Shift-Reduce Parser for a simple grammar, implemented in C++. It uses a parsing table and rule set to process and reduce input strings.

📄 Contents

File Name	Description
Shift_Reduce_Parser.cpp	Main C++ implementation of the shift-reduce parser.
Parsing Table.csv	Parsing table defining actions (shift, reduce, accept, error).
Rules.txt	List of grammar rules used for reductions.

🧠 Learning Goals

• Understand how DFA-based regular language parsers simulate recognition of regular languages.
• Explore the mechanics of bottom-up parsing via the Shift-Reduce technique.
• Gain exposure to parsing table construction, rule-based reduction, and stack-driven parsing logic.