What you See is What you Get: Exploring the Relation between Code Aesthetics and Code Quality

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Abstract

Software artifacts and source code are often viewed as pure technical constructs aiming primarily at delivering specific functionality to the end users. However, almost each line of a computer program is the result of developers' craftsmanship and thus reflects their skills and capabilities, but also their aesthetic view of how code should be written. Additionally, by nature, the code is not an artifact that is managed by a single person: the code is peer-reviewed, in some cases programmed in pairs, or maintained by different people. In this respect, the first impression for the quality of a code is usually a matter of “reading” the aesthetics of the code and then, diving into the details of the actual implementation. This “first-look” impression can psychologically bias the software engineer, either positively or negatively and affect his/her evaluation. In this article we investigate whether code beauty (or code aesthetics) must be valued in software programs, as a proxy to the quality of the code. Specifically, we attempt to relate the notion of code beauty with code quality metrics. For this purpose, we catalogued existing beauty measures (assessing the aesthetics of images, objects, and alphanumeric displays), tailored them to match code beauty, and correlated them to structural properties that are related to Technical Debt Interest (such as coupling, cohesion, etc.). The results of the study suggest that some code beauty metrics can be considered as correlated to TD Interest; and therefore, the “first-look” impression might to some extent be representative of the quality of the reviewed code chunk.

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APA

Maikantis, T., Natsiou, I., Ampatzoglou, A., Chatzigeorgiou, A., Xinogalos, S., & Mittas, N. (2024). What you See is What you Get: Exploring the Relation between Code Aesthetics and Code Quality. In Proceedings - 2024 ACM/IEEE International Conference on Technical Debt, TechDebt 2024 (pp. 1–10). Association for Computing Machinery, Inc. https://doi.org/10.1145/3644384.3644474

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