Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

3-2024

Abstract

Programming problems can be solved in a multitude of functionally correct ways, but the quality of these solutions (e.g. readability, maintainability) can vary immensely. When code quality is poor, symptoms emerge in the form of 'code smells', which are specific negative characteristics (e.g. duplicate code) that can be resolved by applying refactoring patterns. Many undergraduate computing curricula train students on this software engineering practice, often doing so via exercises on unfamiliar instructor-provided code. Our observation, however, is that this makes it harder for novices to internalise refactoring as part of their own development practices. In this paper, we propose a new approach to teaching refactoring, in which students must first complete a programming exercise constrained to ensure they will produce a code smell. This simple intervention is based on the idea that learning refactoring is easier if students are familiar with the code (having built it), that it brings refactoring closer to their regular development practice, and that it presents a powerful opportunity to learn from a 'mistake'. We designed and conducted a study with 35 novice undergraduates in which they completed various refactoring exercises alternately taught using a traditional and our 'mistake-based' approach, finding that students were significantly more effective and confident at completing exercises using the latter.

Keywords

Refactoring, code smells, code quality, software maintenance, software engineering, mistake-based learning, undergraduate course

Discipline

Programming Languages and Compilers | Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

SIGCSE 2024: Proceedings of the 55th ACM Technical Symposium on Computer Science Education, Portland, USA, March 20-23

Volume

1

First Page

1307

Last Page

1313

ISBN

9798400704239

Identifier

10.1145/3626252.3630856

Publisher

ACM

City or Country

New York

Additional URL

https://doi.org/10.1145/3626252.3630856

Download

Share

COinS