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

Overview

Explore how large language models can advance code intelligence for complex software engineering tasks, focusing on symbolic reasoning and global dependency analysis.

Syllabus

• Introduction to Language Models in Software Engineering
Overview of Language Models and AI in code generation
Brief history and evolution of code intelligence tools
• Fundamentals of Large Language Models
Architecture and training of large language models
Understanding transformers and attention mechanisms
• Code Generation with Language Models
Techniques for generating programming code
Evaluating generated code for accuracy and efficiency
• Symbolic Reasoning in Language Models
Definition and importance of symbolic reasoning
Integration of symbolic approaches with language models for software tasks
• Global Dependency Analysis in Software Engineering
Basics of dependency analysis in code
Techniques for leveraging language models in mapping global dependencies
• Advanced Code Intelligence Applications
Using language models to aid in debugging, refactoring, and optimization
Case studies on successful applications of language models in software tasks
• Challenges and Limitations
Current limitations and challenges in code intelligence
Ethical considerations and potential biases in language models
• Future Directions and Innovations
Emerging trends in AI-driven software development
The role of language models in the future of software engineering
• Practical Workshop
Hands-on exercises with code generation tools
Implementing symbolic reasoning and dependency analysis use-cases
• Conclusion
Summary of key learnings
Open discussion and Q&A on the future of code intelligence with language models

Subjects

Programming