Fixing Your Own Smells: Adding a Mistake-Based Familiarisation Step When Teaching Code Refactoring

• URL: http://arxiv.org/abs/2401.01011v1
• Date: Tue, 2 Jan 2024 03:39:19 GMT
• Title: Fixing Your Own Smells: Adding a Mistake-Based Familiarisation Step When Teaching Code Refactoring
• Authors: Ivan Tan, Christopher M. Poskitt
• Abstract summary: Students must first complete a programming exercise to ensure they will produce a code smell. This simple intervention is based on the idea that learning is easier if students are familiar with the code. We conducted a study with 35 novice undergraduates in which they completed various exercises alternately taught using a traditional and our'mistake-based' approach.
• Score: 2.021502591596062
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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