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

סקירה כללית

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

סילבוס

• 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

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נושאים

Programming