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The scent of deep learning code : an empirical study

Hadhemi Jebnoun, Houssem Ben Braiek, Mohammad Masudur Rahman and Foutse Khomh

Paper (2020)

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Deep learning practitioners are often interested in improving their model accuracy rather than the interpretability of their models. As a result, deep learning applications are inherently complex in their structures. They also need to continuously evolve in terms of code changes and model updates. Given these confounding factors, there is a great chance of violating the recommended programming practices by the developers in their deep learning applications. In particular, the code quality might be negatively affected due to their drive for the higher model performance. Unfortunately, the code quality of deep learning applications has rarely been studied to date. In this paper, we conduct an empirical study to investigate the distribution of code smells in deep learning applications. To this end, we perform a comparative analysis between deep learning and traditional open-source applications collected from GitHub. We have several major findings. First, long lambda expression, long ternary conditional expression, and complex container comprehension smells are frequently found in deep learning projects. That is, deep learning code involves more complex or longer expressions than the traditional code does. Second, the number of code smells increases across the releases of deep learning applications. Third, we found that there is a co-existence between code smells and software bugs in the studied deep learning code, which confirms our conjecture on the degraded code quality of deep learning applications.

Uncontrolled Keywords

deep learning; code smells; code quality

Subjects: 2700 Information technology > 2705 Software and development
2700 Information technology > 2706 Software engineering
Department: Department of Computer Engineering and Software Engineering
Funders: GRSNG / NSERC, Fonds de recherche du Québec (FRQ)
PolyPublie URL: https://publications.polymtl.ca/9346/
Conference Title: 17th International Conference on Mining Software Repositories (MSR 2020)
Conference Location: Seoul, Republic of Korea
Conference Date(s): 2020-06-29 - 2020-06-30
Publisher: ACM
DOI: 10.1145/3379597.3387479
Official URL: https://doi.org/10.1145/3379597.3387479
Date Deposited: 07 Sep 2023 09:56
Last Modified: 05 Apr 2024 10:46
Cite in APA 7: Jebnoun, H., Ben Braiek, H., Rahman, M. M., & Khomh, F. (2020, June). The scent of deep learning code : an empirical study [Paper]. 17th International Conference on Mining Software Repositories (MSR 2020), Seoul, Republic of Korea. https://doi.org/10.1145/3379597.3387479



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