SemantiCoder Framework

A state-of-the-art machine learning framework for semantic-aware mutation testing in JavaScript/TypeScript, implementing hybrid transformer-AST architecture with reinforcement learning.

Overview

SemantiCoder addresses the fundamental challenge of generating semantically meaningful mutations for software testing. Unlike traditional mutation testing tools that apply syntactic transformations, SemantiCoder uses deep learning to understand code semantics and generate mutations that are more likely to reveal real bugs.

Key Features

• 🧠 Hybrid Architecture: Combines transformer models with AST structural information
• 🔍 Semantic Awareness: Understands code context, types, and semantic relationships
• 🎯 Intelligent Targeting: Identifies optimal mutation locations using attention mechanisms
• 🚀 Reinforcement Learning: Optimizes for test effectiveness through PPO training
• ⚡ Production Ready: Async inference engine, CLI interface, CI/CD integration
• 🌍 Language Support: JavaScript and TypeScript with extensible architecture

Architecture

Core Components

1. MultiModalEncoder: Fuses code tokens, AST structure, and type information
2. SemanticConstraintLayer: Ensures mutations maintain semantic validity
3. MutationGenerator: Neural model with hierarchical attention for mutation selection
4. Training Pipeline: Three-stage training (pretraining → supervised → RL)

Technical Approach

Input Code → AST Parser → MultiModal Encoder → Attention Layers → Mutation Generator
     ↓              ↓              ↓               ↓              ↓
  Tokenization   Structure    Embeddings    Target Selection   Mutations
                 Analysis                     + Validation

Installation

Prerequisites

• Python 3.8 or higher
• Node.js 16+ (for TypeScript AST parsing)
• CUDA-compatible GPU (recommended)

Quick Install

# Clone the repository
git clone https://github.com/darkomike/semanticoder.git
cd semanticoder

# Install with pip
pip install -e .

# Or install from PyPI
pip install semanticoder

Development Install

# Install with development dependencies
pip install -e ".[dev]"

# Install pre-commit hooks
pre-commit install

# Run tests
pytest

Quick Start

1. Basic Usage

from semanticoder import SemantiCoder

# Initialize the framework
sc = SemantiCoder.from_pretrained("semanticoder/codet5p-js-mutation")

# Generate mutations for a JavaScript file
mutations = sc.generate_mutations("path/to/file.js", num_mutations=10)

# Execute mutations and get results
results = sc.evaluate_mutations(mutations, test_command="npm test")

2. CLI Interface

# Generate mutations
semanticoder generate --input src/ --output mutations/ --num-mutations 50

# Train a custom model
semanticoder train --config configs/training.yaml --data-dir data/

# Evaluate mutations
semanticoder evaluate --mutations mutations/ --tests "npm test"

# Run CI integration
semanticoder ci --threshold 0.8 --report-format json

3. Configuration

# config.yaml
model:
  name: "Salesforce/codet5p-770m"
  max_length: 512
  temperature: 0.7

training:
  batch_size: 8
  learning_rate: 5e-5
  num_epochs: 10
  rl_steps: 1000

mutation:
  semantic_weight: 0.4
  type_weight: 0.3
  context_weight: 0.3
  diversity_threshold: 0.8

Advanced Usage

Custom Training

from semanticoder.training import TrainingPipeline
from semanticoder.data import MutationDataset

# Prepare your dataset
dataset = MutationDataset.from_repository("path/to/bug/repos")

# Configure training pipeline
pipeline = TrainingPipeline(
    model_name="Salesforce/codet5p-770m",
    config_path="configs/custom_training.yaml"
)

# Stage 1: Pretraining
pipeline.pretrain(dataset.pretraining_data)

# Stage 2: Supervised Fine-tuning
pipeline.supervised_finetune(dataset.supervised_data)

# Stage 3: Reinforcement Learning
pipeline.rl_finetune(
    dataset.rl_data,
    reward_function=custom_reward_function
)

CI/CD Integration

GitHub Actions

name: SemantiCoder Mutation Testing
on: [push, pull_request]

jobs:
  mutation-testing:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3
      - name: Setup SemantiCoder
        run: pip install semanticoder
      - name: Run Mutation Testing
        run: |
          semanticoder ci \
            --input src/ \
            --tests "npm test" \
            --threshold 0.8 \
            --report-format github

Jenkins

pipeline {
    agent any
    stages {
        stage('Mutation Testing') {
            steps {
                sh 'pip install semanticoder'
                sh 'semanticoder ci --config jenkins.yaml'
                publishHTML([
                    allowMissing: false,
                    alwaysLinkToLastBuild: true,
                    keepAll: true,
                    reportDir: 'reports',
                    reportFiles: 'mutation-report.html',
                    reportName: 'Mutation Testing Report'
                ])
            }
        }
    }
}

Research Background

SemantiCoder is based on cutting-edge research in:

• Neural Program Analysis: Using transformers to understand code semantics
• Mutation Testing: Improving fault detection through intelligent mutation generation
• Reinforcement Learning: Optimizing mutation strategies through reward signals
• Multi-modal Learning: Combining textual and structural code representations

Key Publications

• "Semantic-Aware Mutation Testing with Deep Learning" (2024)
• "Hybrid Transformer-AST Models for Code Analysis" (2023)
• "Reinforcement Learning for Test Generation" (2023)

Performance

Benchmarks

*Trade-off for quality - semantic analysis requires more computation

Hardware Requirements

• Minimum: 8GB RAM, 4-core CPU
• Recommended: 16GB RAM, 8-core CPU, GPU with 8GB VRAM
• Production: 32GB RAM, 16-core CPU, Multi-GPU setup

API Reference

Core Classes

• SemantiCoder: Main framework interface
• MultiModalEncoder: Encoder for code, AST, and type information
• MutationGenerator: Neural mutation generation model
• TrainingPipeline: End-to-end training system
• InferenceEngine: Async mutation generation engine

Configuration

• ModelConfig: Model architecture configuration
• TrainingConfig: Training pipeline settings
• MutationConfig: Mutation generation parameters
• CIConfig: CI/CD integration settings

Contributing

We welcome contributions! Please see our Contributing Guide for details.

Development Setup

git clone https://github.com/darkomike/semanticoder.git
cd semanticoder
pip install -e ".[dev]"
pre-commit install

Testing

pytest tests/
pytest --cov=semanticoder tests/

License

This project is licensed under the MIT License - see the LICENSE file for details.

Citation

@software{semanticoder2024,
  title={SemantiCoder: A Machine Learning Framework for Semantic-Aware Mutation Testing},
  author={SemantiCoder Research Team},
  year={2025},
  url={https://github.com/darkomike/semanticoder}
}

Support

• 💬 Discussions
• 🐛 Issues
• 📧 Email

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Note: This is a research project. For production use, please thoroughly evaluate the generated mutations and their impact on your testing strategy.