Copilot Tutor: Automated Software Engineering Practice
Augmented with LLMs
Name
kong-blisse-meng-eecs-2025-thesis.pdf
Description
Thesis PDF
Size
2.7 MB
Format
Adobe PDF
Checksum (MD5)
dbab0ff27cab76b90f0bb0d8e62d1d6f
Author(s)
Kong, Blisse
Advisor(s)
Miller, Robert C.
Date Issued
May 2025
Publisher
Massachusetts Institute of Technology
Abstract
In recent years, large language models (LLMs) have become more ubiquitous in the workplace. In software engineering, they are often realized as “copilots" which produce code given a prompt or existing code. Programmers using these tools to increase their coding productivity need to be proficient in inspecting and in understanding these copilots’ outputs. As engineers incorporate these tools to accelerate their workflows, they have a parallel opportunity to accelerate learning new programming languages. This thesis presents a tutor interface where students with some programming experience in an origin language can learn a target language while practicing how to critically read and fix a copilot’s output to write correct, safe programs. This work also introduces the automatic generation of exercises teaching syntax and semantics on which a programmer experienced in the origin language but not the target language should focus.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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In Copyright - Educational Use Permitted
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