KG2Lib: knowledge-graph-based convolutional network for third-party library recommendation.
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| Title: | KG2Lib: knowledge-graph-based convolutional network for third-party library recommendation. |
|---|---|
| Authors: | Zhao, Jing-zhuan1 (AUTHOR), Zhang, Xuan1,2,3 (AUTHOR) zhxuan@ynu.edu.cn, Gao, Chen4 (AUTHOR), Li, Zhu-dong1 (AUTHOR), Wang, Bao-lei1 (AUTHOR) |
| Source: | Journal of Supercomputing. Jan2023, Vol. 79 Issue 1, p1-26. 26p. |
| Subjects: | Library information networks, Library software, Knowledge graphs, Systems software, Graph algorithms |
| Abstract: | In the process of software system evolution, software users constantly put forward a large number of expectations. For these expectations, software developers usually use the existing third-party libraries and other software resources to accelerate their development processes. At present, tons of third-party libraries are available. Therefore, appropriate recommendation methods are very important for developers to find suitable libraries for their development projects. In this paper, we present KG2Lib, a recommendation method to assist software developers in selecting suitable software libraries for their current projects. KG2Lib exploits a knowledge-graph-based convolutional network to recommend software libraries by relying on a set of libraries which were already called by current projects. The interaction matrix, weight matrix and knowledge graph are the inputs of KG2Lib. What's more, KG2Lib recommends libraries to developers from project level and library level, which can better capture the fine-grained information to achieve better recommend performance. The performance of KG2Lib was evaluated on three datasets with four existing baseline models. The experimental results show that KG2Lib achieves better performance and helps software developers accurately select the appropriate third-party libraries. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Supercomputing is the property of Springer Nature 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 |
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| Header | DbId: egs DbLabel: Engineering Source An: 161138693 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: KG2Lib: knowledge-graph-based convolutional network for third-party library recommendation. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Zhao%2C+Jing-zhuan%22">Zhao, Jing-zhuan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhang%2C+Xuan%22">Zhang, Xuan</searchLink><relatesTo>1,2,3</relatesTo> (AUTHOR)<i> zhxuan@ynu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Gao%2C+Chen%22">Gao, Chen</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Zhu-dong%22">Li, Zhu-dong</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Bao-lei%22">Wang, Bao-lei</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Supercomputing%22">Journal of Supercomputing</searchLink>. Jan2023, Vol. 79 Issue 1, p1-26. 26p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Library+information+networks%22">Library information networks</searchLink><br /><searchLink fieldCode="DE" term="%22Library+software%22">Library software</searchLink><br /><searchLink fieldCode="DE" term="%22Knowledge+graphs%22">Knowledge graphs</searchLink><br /><searchLink fieldCode="DE" term="%22Systems+software%22">Systems software</searchLink><br /><searchLink fieldCode="DE" term="%22Graph+algorithms%22">Graph algorithms</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: In the process of software system evolution, software users constantly put forward a large number of expectations. For these expectations, software developers usually use the existing third-party libraries and other software resources to accelerate their development processes. At present, tons of third-party libraries are available. Therefore, appropriate recommendation methods are very important for developers to find suitable libraries for their development projects. In this paper, we present KG2Lib, a recommendation method to assist software developers in selecting suitable software libraries for their current projects. KG2Lib exploits a knowledge-graph-based convolutional network to recommend software libraries by relying on a set of libraries which were already called by current projects. The interaction matrix, weight matrix and knowledge graph are the inputs of KG2Lib. What's more, KG2Lib recommends libraries to developers from project level and library level, which can better capture the fine-grained information to achieve better recommend performance. The performance of KG2Lib was evaluated on three datasets with four existing baseline models. The experimental results show that KG2Lib achieves better performance and helps software developers accurately select the appropriate third-party libraries. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Supercomputing is the property of Springer Nature 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.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s11227-022-04603-3 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 26 StartPage: 1 Subjects: – SubjectFull: Library information networks Type: general – SubjectFull: Library software Type: general – SubjectFull: Knowledge graphs Type: general – SubjectFull: Systems software Type: general – SubjectFull: Graph algorithms Type: general Titles: – TitleFull: KG2Lib: knowledge-graph-based convolutional network for third-party library recommendation. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Zhao, Jing-zhuan – PersonEntity: Name: NameFull: Zhang, Xuan – PersonEntity: Name: NameFull: Gao, Chen – PersonEntity: Name: NameFull: Li, Zhu-dong – PersonEntity: Name: NameFull: Wang, Bao-lei IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: Jan2023 Type: published Y: 2023 Identifiers: – Type: issn-print Value: 09208542 Numbering: – Type: volume Value: 79 – Type: issue Value: 1 Titles: – TitleFull: Journal of Supercomputing Type: main |
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