TraceMate: Collaborating with AI in Test-Driven Programming

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

Abstract

Large programming courses often use test-driven autograding systems (e.g., CodeRunner) for instant feedback, but these usually only show which tests failed without explaining why or how to fix the error. Novice students struggle with such minimal guidance and often resort to trial-and-error. Meanwhile, AI coding assistants (e.g., GitHub Copilot, ChatGPT) can provide hints and code suggestions, but novices may over-trust these outputs and lack the skills to verify them. To solve these issues, we present TraceMate, an IDE plugin that pairs the autograder’s tests with a conversational AI chatbot. TraceMate augments test feedback with context-aware explanations and inline suggestions for code modifications, and immediately validates each AI suggestion on the test suite. This workflow gives actionable hints while ensuring any AI-proposed changes are correct. In a user study with novice programmers, participants using TraceMate solved problems more effectively and reported higher confidence than those using only the autograder. These results suggest that pairing automated tests with an interactive AI assistant can enhance learning in introductory programming courses.
Original languageEnglish
Title of host publicationVL/HCC 2025 - The IEEE Symposium on Visual Languages and Human-Centric Computing
PublisherIEEE Computer Science Society
Number of pages7
Publication statusAccepted/In press - 19 Jul 2025
EventThe 2025 IEEE Symposium on Visual Languages and Human-Centric Computing - Raleigh, United States
Duration: 7 Oct 202510 Oct 2025
Conference number: 41
https://conf.researchr.org/home/vlhcc-2025

Conference

ConferenceThe 2025 IEEE Symposium on Visual Languages and Human-Centric Computing
Abbreviated titleVL/HCC 2025
Country/TerritoryUnited States
CityRaleigh
Period7/10/2510/10/25
Internet address

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