Building Developer Assistants that Think Fast and Slow

Explore the design of developer assistants that combine the strengths of fast, automatic thinking (System 1) with slower, more deliberate analysis (System 2), to create powerful tools for coding and software development.

Key takeaways

System 1 and System 2 in Developer Assistants
- System 1 operates quickly and automatically, using habitual patterns and rules
- System 2 is slower and more deliberate, using logical reasoning and working memory
Limitations of System 1-based Models
- Limited by lack of explicit knowledge and domain expertise
- Prone to errors and bias
- Can only generate a single answer or solution
Advantages of System 2-based Models
- Can reason about abstract concepts and complex problems
- Can generate multiple solutions and alternatives
- Can learn from errors and improve over time
Combining System 1 and System 2
- Can leverage the strengths of both systems to create more powerful developer assistants
- Can use system 1 to generate initial hypotheses and system 2 to refine and modify them
Data Quality and Collection
- High-quality data is essential for training and improving system 1 and system 2 models
- Data collection methods include mining GitHub commit logs and using predefined datasets
- Data quality issues include noise and bias
Error Handling and Feedback
- System 1-based models can make mistakes and generate incorrect results
- System 2-based models can be designed to recover from errors and improve over time
- Feedback loops can be used to refine and adjust model performance
Applications and Future Directions
- Developer assistants can be used for a variety of tasks, including code completion, debugging, and code search
- Future directions include exploring new architectures and techniques, such as graph neural networks and reinforcement learning

Building Developer Assistants that Think Fast and Slow

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