From Code Generation Towards Software Engineering: Advancing Code Intelligence with Language Models

Overview

Syllabus

• Introduction to Code Intelligence
Overview of Language Models in AI
Historical Context: From Rule-based Systems to Neural Networks
• Fundamentals of Code Generation with Language Models
Basics of Transformer Architecture
Pre-training and Fine-tuning for Code Completion
Limitations of Current Code Generation Techniques
• Symbolic Reasoning in Language Models
Introduction to Symbolic AI Concepts
Integrating Symbolic Reasoning with Neural Models
Case Studies: Symbolic Enhancement in Code Analysis
• Global Reasoning for Software Engineering Tasks
Understanding Software Engineering Complexity
Role of Global Context in Code Comprehension
Techniques for Capturing Global Code Structure
• Advanced Applications in Software Engineering
Automated Debugging and Error Correction
Assistive Models for Software Design and Architecture
Enhancing Model Performance on Legacy Codebases
• Challenges and Future Directions
Ethical Considerations in AI-driven Software Development
Limitations of Current AI Techniques in SE
Future Prospects: Unifying Symbolic and Neural Approaches
• Practical Workshop: Building and Deploying a Code Intelligence Model
Tools and Frameworks: PyTorch, TensorFlow, and Hugging Face
Hands-on Project: Developing a Code Suggestion System
• Conclusion and Next Steps
Recap of Key Learnings
Directions for Continued Learning and Research Opportunities

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Subjects

Computer Science