Winter 2023
Designing with Large Language Models for Debugging Assistance
Instructors:
Dr. Colin Clement (Microsoft)
Dr. David W. McDonald
Note this DRG is at capacity for winter quarter and no longer accepting applications.
Being good at programming is partly a function of what you are taught in a course and partly the experiences you gain. Debugging a program when something goes wrong is often based on hard won experiences.
What if we could make some aspects of debugging easier?
This DRG will consider how to improve the debugging experiences of novice programmers using large language models (LLMs)---such as OpenAI's Codex---which can answer questions and offer edit suggestions leveraging both natural language and source code.
Software flaws or errors, sometimes generate 'exceptions' which often contain code context and dubiously helpful error messages. This DRG will use the knowledge retrieval and synthesis behaviors of LLMs to offer suggestions to overcome such errors quickly, inside the development environment.
In the DRG students will develop interactive prototypes for an IDE to capture exceptions, interact with an LLM and display possible solutions. These interactive prototypes will explore the possible user experiences that will help novice programmers overcome challenges and learn to unblock themselves.
Students who are the best fit for this DRG will minimally:
- Have had 2+ programming courses
- Have experience with prototyping techniques
- Have used Python
This DRG will meet Tuesdays, from 4 - 5:30 p.m.
Note this DRG is at capacity for winter quarter and no longer accepting applications.