Exploring Neural Network Structure Code Reuse in the Open‐Source Community for Improving Maintenance.
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| Title: | Exploring Neural Network Structure Code Reuse in the Open‐Source Community for Improving Maintenance. |
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| Authors: | Ren, Xiaoning1 (AUTHOR), Wang, Yuekun2 (AUTHOR), Liu, Chongyang1 (AUTHOR) lcyyy@mail.ustc.edu.cn, Wu, Yueming3 (AUTHOR), Hu, Qiang4 (AUTHOR), Zhang, Lijun5 (AUTHOR), Xue, Yinxing6 (AUTHOR) yxxue@iaii.ac.cn |
| Source: | Journal of Software: Evolution & Process. Mar2026, Vol. 38 Issue 3, p1-17. 17p. |
| Subjects: | Computer software reusability, Software maintenance, Artificial neural networks, Intellectual property, Open source software, Software architecture |
| Abstract: | Neural networks (NNs) have rapidly advanced, demonstrating exceptional performance across various fields, leading to a surge in open‐source NN projects. The complexity and rapid growth of these projects pose significant challenges for maintenance within the open‐source community. Given that NN architecture code is the core asset of NN projects, understanding its reuse in the open‐source community is essential for effective maintenance, such as reducing redundancy and identifying potential intellectual property violations. While prior studies have examined code reuse in open‐source projects, they have two key limitations: They do not specifically address NN structure code, and they rely on manually selected small‐scale datasets that do not sufficiently represent the broader open‐source ecosystem. To address these limitations, this study explores reuse patterns in a large‐scale dataset of 20,000 open‐source projects on GitHub, focusing specifically on NN structure code. Specially, we categorize NN structure reuse into three types: (1) exact reuse with no changes; (2) shallow reuse with minor adjustments like renaming variables or adjusting parameters; and (3) conceptual reuse with significant modifications, while retaining the same layer sequence. We then propose a detection framework, NNReuse, to identify these reuse types and conduct an empirical evaluation of their prevalence and characteristics. As a practical application, we also assess potential license conflicts based on NNReuse. Extensive experiments show that 55.6% of projects and 54.17% of NN structures exhibit structural similarities that are consistent with potential NN structure reuse in open‐source projects. Among these, exact reuse is particularly common and introduces significant redundancy, with an estimated storage optimization potential of up to 34.49%. Reuse primarily occurs at a high level, with 43.3% involving the reuse of overall network architecture. Additionally, in projects with license protection, as much as 64.3% may present potential license conflicts, highlighting the importance of strengthened license compliance and proactive IP risk mitigation in the open‐source community. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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