Techta/backend-code-generator-dataset

Backend Code Generation Dataset

Dataset Description

This dataset contains examples for training AI models to generate backend application code. It includes descriptions of backend requirements paired with complete, functional code implementations across multiple frameworks and programming languages.

Dataset Summary

The Backend Code Generation Dataset is designed to train models that can generate complete backend applications from natural language descriptions. The dataset covers popular backend frameworks including Express.js, FastAPI, Django, Flask, and others, with implementations in JavaScript, Python, and Go.

Supported Tasks and Leaderboards

• Primary Task: Code Generation
• Secondary Tasks: Text-to-Code Translation, API Generation
• Languages: JavaScript, Python, Go
• Frameworks: Express.js, FastAPI, Django, Flask, NestJS, Gin

Languages

The dataset primarily uses English for descriptions and requirements, with code implementations in:

• JavaScript (Node.js)
• Python
• Go

Dataset Structure

Data Instances

Each example in the dataset contains:

{
  "description": "E-commerce API with user authentication and product management",
  "requirements": [
    "User registration and login",
    "JWT token authentication", 
    "Product CRUD operations",
    "Order management",
    "Input validation"
  ],
  "framework": "fastapi",
  "language": "python",
  "complexity": "medium",
  "estimated_lines": 250,
  "code_files": {
    "main.py": "from fastapi import FastAPI, Depends...",
    "models.py": "from sqlalchemy import Column, Integer...",
    "auth.py": "from passlib.context import CryptContext...",
    "requirements.txt": "fastapi==0.68.0\nuvicorn==0.15.0..."
  },
  "features": [
    "authentication",
    "crud_operations", 
    "database_integration",
    "input_validation",
    "error_handling"
  ]
}

Data Fields

• description (string): Natural language description of the backend application requirements
• requirements (list): Specific functional requirements broken down into bullet points
• framework (string): Target backend framework (express, fastapi, django, flask, nestjs, gin)
• language (string): Programming language (javascript, python, go)
• complexity (string): Complexity level (simple, medium, complex)
• estimated_lines (integer): Approximate number of lines in the generated code
• code_files (dict): Dictionary mapping file names to their complete code content
• features (list): Tags indicating what features are implemented in the code

Data Splits

The dataset is split as follows:

• Train: 800 examples (80%)
• Validation: 100 examples (10%)
• Test: 100 examples (10%)

Dataset Creation

Curation Rationale

This dataset was created to address the need for high-quality training data for backend code generation models. Existing code datasets often lack the structure and completeness needed for generating full backend applications. This dataset provides:

1. Complete Applications: Each example contains a full, runnable backend application
2. Multi-Framework Coverage: Supports the most popular backend frameworks
3. Structured Requirements: Clear mapping from natural language to code features
4. Best Practices: Generated code follows industry standards and security practices

Source Data

Initial Data Collection and Normalization

The dataset was created through a combination of:

1. GitHub Repository Mining: Collected and analyzed popular backend repositories
2. Synthetic Generation: Created examples following common backend patterns
3. Expert Review: Manual validation and improvement of generated examples

Who are the source language producers?

• GitHub Repositories: Open source projects from the developer community
• Synthetic Generation: Programmatically generated following established patterns
• Expert Curation: Professional backend developers with 5+ years experience

Annotations

Annotation process

1. Automated Extraction: Code patterns and structures extracted from repositories
2. Feature Tagging: Automatic identification of implemented features
3. Complexity Assessment: Algorithmic complexity scoring based on code metrics
4. Manual Review: Expert validation of code quality and completeness

Who are the annotators?

• Backend development experts
• AI/ML engineers specializing in code generation
• Software architecture consultants

Personal and Sensitive Information

The dataset has been carefully reviewed to ensure:

• No personal information (names, emails, passwords) in code examples
• No real API keys or authentication tokens
• No proprietary business logic or sensitive algorithms
• All database examples use generic schemas

Considerations for Using the Data

Social Impact of Dataset

Positive Impacts:

• Democratizes backend development knowledge
• Helps developers learn best practices across frameworks
• Accelerates prototyping and development workflows
• Provides educational resource for learning backend development

Potential Negative Impacts:

• May reduce demand for entry-level backend developers
• Generated code might not always follow latest security practices
• Could perpetuate existing biases in code patterns

Discussion of Biases

The dataset may contain biases toward:

• Popular frameworks (Express.js, FastAPI) over niche alternatives
• Western development practices and patterns
• English-language variable and function naming conventions
• Specific architectural patterns common in open-source projects

Other Known Limitations

• Framework Versions: Examples may not reflect the latest framework versions
• Security Practices: While best practices are followed, security landscapes evolve rapidly
• Scalability: Examples focus on standard use cases, may not cover high-scale scenarios
• Testing: Limited test code generation compared to application code
• Database Diversity: Primarily uses PostgreSQL/MongoDB, limited NoSQL variety

Additional Information

Dataset Curators

This dataset was curated by the Backend AI Training Team, consisting of:

• Senior Backend Engineers
• Machine Learning Researchers
• Developer Education Specialists

Licensing Information

This dataset is released under the MIT License, allowing for:

• Commercial and non-commercial use
• Modification and distribution
• Private use
• Patent use protection

Citation Information

@dataset{backend_code_generation_2024,
  title={Backend Code Generation Dataset},
  author={Backend AI Training Team},
  year={2024},
  publisher={Hugging Face},
  url={https://huggingface.co/datasets/your-username/backend-code-generation}
}

Contributions

We welcome contributions to improve this dataset:

1. Quality Issues: Report code examples that don't run or have security issues
2. Framework Coverage: Suggest additional frameworks to include
3. Feature Requests: Propose new types of backend applications to cover
4. Bias Reporting: Help identify and address biases in the dataset

Please open issues or pull requests in the dataset repository.

Changelog

v1.0.0 (2025-09-04)

• Initial release with 1000 examples
• Coverage: Express.js, FastAPI, Django, Flask
• Languages: JavaScript, Python, Go
• Features: Authentication, CRUD, Database integration

Contact Information

For questions, suggestions, or collaboration opportunities:

• Email: peterglo2200@gmail.com
• GitHub: https://github.com/PetersGlory/backend-code-generator-model
• Discord: Backend AI Community Server

Acknowledgments

Special thanks to:

• The open-source community for providing reference implementations
• Framework maintainers for excellent documentation
• Beta testers who provided valuable feedback
• Hugging Face for hosting and infrastructure support