Reseach work - quanwangniuniu/Z_Language GitHub Wiki

Z Language Development Plan

This document outlines the development plan for the Z Language, focusing on language design, parsing, semantic analysis, code generation, and AI integration. All implementation is based on Python. The content is divided into contributions from team members, with tasks and timelines clearly defined.

1. Guorui Li: Programming Language Theory Foundations

1.1 Formal Language Theory

Why Learn: Understanding the formal definition of languages is the foundation for designing syntax and parsers.

Content:

  • Formal grammars and automata theory
  • Regular languages and context-free languages
  • Grammar notations (EBNF, BNF)
  • Classification and hierarchy of formal languages

Recommended Resources:

  • Compilers: Principles, Techniques, and Tools (Dragon Book) - Chapters 2, 3
  • Stanford CS143 Course
  • Paper: "Formal Language Theory and Language Design" by Noam Chomsky

Practice Suggestions:

  • Define a simple calculator grammar using EBNF
  • Identify different language types (regular, context-free, recursively enumerable)
  • Analyze the syntax rules of existing programming languages

1.2 Programming Language Design Principles

Why Learn: Helps establish core design principles and syntactic features for the Z Language.

Content:

  • Trade-offs in language design (simplicity vs. expressiveness)
  • Syntactic consistency and orthogonality
  • Abstraction mechanisms (variables, functions, types, etc.)
  • Error handling and recovery strategies

Recommended Resources:

  • Programming Language Pragmatics by Michael Scott
  • Beautiful Code by Andy Oram & Greg Wilson
  • "Growing a Language" Talk by Guy Steele

Practice Suggestions:

  • Analyze design decisions in Python, JavaScript, etc.
  • Compare error handling mechanisms across languages
  • Evaluate usability of language features from a user perspective

2. LIUHAOQING: AI Integration into Compilers

Third Stage: AI Integration and Advanced Features

5.1 Natural Language Processing (NLP) Basics

Why Learn: Understanding NLP techniques for parsing user requirements.

Content:

  • Text classification and tokenization
  • Semantic parsing basics
  • Named entity recognition
  • Dependency parsing

Recommended Resources:

Practice Suggestions:

  • Use NLTK/spaCy to analyze simple requirement texts
  • Implement keyword extraction and intent recognition
  • Build a simple semantic parser

5.2 Large Language Model (LLM) API Integration

Why Learn: Integrate modern LLMs into the Z Language to provide AI-assisted features.

Content:

  • Using OpenAI API
  • Prompt engineering techniques
  • Optimizing API requests
  • Parsing and processing results

Recommended Resources:

Practice Suggestions:

  • Implement basic LLM API calls
  • Design effective prompts for code generation
  • Handle API limitations and errors

5.3 Requirement-to-Code Conversion

Why Learn: Enable the core functionality of the Z Language—converting natural language requirements into executable code.

Content:

  • Requirement analysis and decomposition
  • Semantic mapping techniques
  • Code synthesis strategies
  • Result validation and improvement

Recommended Resources:

Practice Suggestions:

  • Build requirement templates for common tasks
  • Implement requirement decomposition and mapping algorithms
  • Create a test set to evaluate conversion quality

5.4 AI-Assisted Error Correction

Why Learn: Implement AI-driven error correction in the Z Language to reduce debugging time.

Content:

  • Error pattern recognition
  • Context-aware repair generation
  • Ranking repair suggestions
  • User interaction design

Recommended Resources:

Practice Suggestions:

  • Collect common error patterns
  • Implement error detection and repair generation
  • Design user-friendly repair suggestion displays

3. Lihanwen: Lexical Analysis and Parsing Techniques

2.1 Lexical Analysis

Why Learn: The first step in the Z Language parser is converting source code into a sequence of tokens.

Content:

  • Regular expression theory and practice
  • Deterministic Finite Automata (DFA) and Non-deterministic Finite Automata (NFA)
  • Lexical analyzer generation tools (Lex/Flex/PLY)
  • Tokenization strategies and error recovery

Recommended Resources:

Practice Suggestions:

  • Manually implement a simple lexical analyzer
  • Use PLY to define token patterns for the Z Language
  • Handle special cases (comments, strings, indentation, etc.)

2.2 Syntax Analysis (Parsing)

Why Learn: Converting token sequences into an Abstract Syntax Tree (AST) is critical for understanding code structure.

Content:

  • Top-down parsing (recursive descent, LL parsing)
  • Bottom-up parsing (LR, LALR parsing)
  • Parser combinator methods
  • Parser generation tools (Yacc/Bison/ANTLR)
  • Error detection and recovery strategies

Recommended Resources:

Practice Suggestions:

  • Implement a recursive descent parser for arithmetic expressions
  • Use ANTLR or PLY to generate a parser for Z Language’s basic syntax
  • Design effective syntax error reporting mechanisms

2.3 Abstract Syntax Tree (AST) Design

Why Learn: The AST is an intermediate representation of code structure, impacting all subsequent processing steps.

Content:

  • AST node design and class hierarchy
  • Visitor pattern and tree traversal
  • Attribute grammars and symbol tables
  • AST visualization and debugging

Recommended Resources:

Practice Suggestions:

  • Design AST node classes for Z Language’s core constructs
  • Implement the visitor pattern to handle different node types
  • Build an AST visualization tool for debugging

Second Stage: Semantic Analysis and Code Generation

3.1 Type Systems and Type Checking

Why Learn: Even with dynamic typing in the Z Language, understanding type systems helps catch errors and optimize.

Content:

  • Static vs. dynamic type systems
  • Type inference algorithms
  • Polymorphism and generics
  • Implementing type checking

Recommended Resources:

Practice Suggestions:

  • Implement a simple type inference system
  • Design type error reporting mechanisms for the Z Language
  • Explore gradual typing possibilities

3.2 Symbol Resolution and Scoping

Why Learn: Handle variable binding, name resolution, and scoping rules.

Content:

  • Symbol table design and implementation
  • Lexical vs. dynamic scoping
  • Name resolution algorithms
  • Scope nesting and closures

Recommended Resources:

Practice Suggestions:

  • Implement a symbol table for nested scopes
  • Handle variable declarations, references, and shadowing
  • Test complex scoping scenarios

3.3 Semantic Error Detection

Why Learn: Catch syntactically correct but semantically incorrect code and provide useful error messages.

Content:

  • Common semantic error types
  • Error detection algorithms
  • Error message generation
  • Custom error handling

Recommended Resources:

Practice Suggestions:

  • Design an error code system for the Z Language
  • Implement detailed and helpful error messages
  • Create test cases for common errors

4. Liuziyang: Weekly Plan and Team Coordination

First Week: Language Design and Basic Parsing

  • Formal language theory basics (1 day)
  • Programming language design principles (1 day)
  • Lexical analyzer implementation (2 days)
  • Basic syntax parser implementation (2 days)

Second Week: AST and Code Generation

  • AST design and implementation (2 days)
  • Semantic analysis basics (1 day)
  • Python code generation (2 days)
  • Simple execution environment (1 day)

Note: All development is based on Python.

Reporting Structure

  1. Daily Standup Meetings: 15 minutes
    • No comments; only one core issue discussed
  2. Weekly Team Meetings: 60 minutes
    • Document and code reporting
  3. Code Review: Conducted during weekly team meetings

Deliverables

  • Produce Markdown documentation
  • Share with the team
  • Ensure team members understand principles clearly, keeping explanations simple and focused on what needs to be done

This Week’s Goals

  • Understand what we are building
  • Identify tools and technologies to use
  • Outline the development process
  • Define expected outcomes
  • Establish testing standards
  • Plan AI integration

Next Team Meeting

  • Date/Time: Friday, 9:00 PM Beijing Time

This document is formatted for inclusion in the GitHub wiki, maintaining the original structure and content in English. Each team member’s contributions and tasks are clearly outlined.