DCC Sidekick: Helping Novices Solve Programming Errors Through a Conversational Explanation Interface

Abstract

Students in introductory computing courses often lack the experience required to effectively identify and resolve errors in their code. For such students, Programming Error Messages (PEMs) are often the first indication of an error, and could provide valuable debugging guidance. However, in many cases, such as with standard C compiler implementations, PEMs are largely unsuitable for novices. Confusing, misleading, and filled with terse language and jargon, these messages instead act as an additional source of difficulty. In this paper, we present DCC Sidekick, which integrates the Debugging C Compiler (DCC) with a Large Language Model (LLM) in a web-based dashboard to produce contextual, accurate guidance conducive to student learning. This dashboard is directly accessible from the output of the compiler, and provides a bird's-eye-view of the program source, compiler output, and a conversational AI interface to help unravel cryptic error messages. We aim to deploy DCC Sidekick to a C-based CS1 cohort at a large higher education institution to investigate how novice students utilise the conversational explanation interface during debugging activities. In this work, we present our experience designing and building DCC Sidekick.