Exploring Neural Network Structure Code Reuse in the Open‐Source Community for Improving Maintenance.
Saved in:
| Title: | Exploring Neural Network Structure Code Reuse in the Open‐Source Community for Improving Maintenance. |
|---|---|
| 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] |
| Copyright of Journal of Software: Evolution & Process is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
|---|---|
| Header | DbId: egs DbLabel: Engineering Source An: 192557025 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Exploring Neural Network Structure Code Reuse in the Open‐Source Community for Improving Maintenance. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Ren%2C+Xiaoning%22">Ren, Xiaoning</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Yuekun%22">Wang, Yuekun</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Liu%2C+Chongyang%22">Liu, Chongyang</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> lcyyy@mail.ustc.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Wu%2C+Yueming%22">Wu, Yueming</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Hu%2C+Qiang%22">Hu, Qiang</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhang%2C+Lijun%22">Zhang, Lijun</searchLink><relatesTo>5</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Xue%2C+Yinxing%22">Xue, Yinxing</searchLink><relatesTo>6</relatesTo> (AUTHOR)<i> yxxue@iaii.ac.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Software%3A+Evolution+%26+Process%22">Journal of Software: Evolution & Process</searchLink>. Mar2026, Vol. 38 Issue 3, p1-17. 17p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Computer+software+reusability%22">Computer software reusability</searchLink><br /><searchLink fieldCode="DE" term="%22Software+maintenance%22">Software maintenance</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Intellectual+property%22">Intellectual property</searchLink><br /><searchLink fieldCode="DE" term="%22Open+source+software%22">Open source software</searchLink><br /><searchLink fieldCode="DE" term="%22Software+architecture%22">Software architecture</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Software: Evolution & Process is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=192557025 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1002/smr.70090 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 17 StartPage: 1 Subjects: – SubjectFull: Computer software reusability Type: general – SubjectFull: Software maintenance Type: general – SubjectFull: Artificial neural networks Type: general – SubjectFull: Intellectual property Type: general – SubjectFull: Open source software Type: general – SubjectFull: Software architecture Type: general Titles: – TitleFull: Exploring Neural Network Structure Code Reuse in the Open‐Source Community for Improving Maintenance. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Ren, Xiaoning – PersonEntity: Name: NameFull: Wang, Yuekun – PersonEntity: Name: NameFull: Liu, Chongyang – PersonEntity: Name: NameFull: Wu, Yueming – PersonEntity: Name: NameFull: Hu, Qiang – PersonEntity: Name: NameFull: Zhang, Lijun – PersonEntity: Name: NameFull: Xue, Yinxing IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 03 Text: Mar2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 20477473 Numbering: – Type: volume Value: 38 – Type: issue Value: 3 Titles: – TitleFull: Journal of Software: Evolution & Process Type: main |
| ResultId | 1 |
