📝 SummarXiv

Abstract

Motivation. Trust in generative AI programming assistants is a vital attitude that impacts how programmers use those programming assistants. Programmers that are over-trusting may be too reliant on their tools, leading to incorrect or vulnerable code; programmers that are under-trusting may avoid using tools that can improve their productivity and well-being. Methods. Since trust is a dynamic attitude that may change over time, this study aims to understand programmers' evolution of trust after immediate (one hour) and extended (10 days) use of GitHub Copilot. We collected survey data from 71 upper-division computer science students working on a legacy code base, representing a population that is about to enter the workforce. In this study, we quantitatively measure student trust levels and qualitatively uncover why student trust changes. Findings. Student trust, on average, increased over time. After completing a project with Copilot, however, students felt that Copilot requires a competent programmer to complete some tasks manually. Students mentioned that seeing Copilot's correctness, understanding how Copilot uses context from the code base, and learning some basics of natural language processing contributed to their elevated trust. Implications. Our study helps instructors and industry managers understand the factors that influence how students calibrate their trust with AI assistants. We make four pedagogical recommendations, which are that CS educators should 1) provide opportunities for students to work with Copilot on challenging software engineering tasks to calibrate their trust, 2) teach traditional skills of comprehending, debugging, and testing so students can verify output, 3) teach students about the basics of natural language processing, and 4) explicitly introduce and demonstrate the range of features available in Copilot.