A Visualised Software Library: Nested Self-Organising Maps for Retrieving and Browsing Reusable Software Assets.
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| Title: | A Visualised Software Library: Nested Self-Organising Maps for Retrieving and Browsing Reusable Software Assets. |
|---|---|
| Authors: | Ye, H., Lo, B. W. N. |
| Source: | Neural Computing & Applications. 2000, Vol. 9 Issue 4, p266. 14p. |
| Subjects: | Computer software, Self-organizing maps, Self-organizing systems |
| Abstract: | This paper presents an approach to self-structuring software libraries. The authors developed a representation scheme to construct a feature space over a collection of software assets. The feature space is represented and classified by a variety of the self-organising map, called the Nested Software Self-Organising Map (NSSOM), consisting of a top map and a set of sub-maps nested in the top map. The clustering on the top map provides general improvements in retrieval recall, while the lower-level nested maps further elaborate the clusters' into more specific groups enhancing retrieval precision. The results of preliminary evaluation showed that NSSOM is capable of enhancing precision without sacrificing recall. In addition, a user-friendly browsing facility has also been developed which helps users predict the desired components by providing an intelligible search space. The present approach attempts to achieve an optimal combination of efficiency, accuracy and user-friendliness, which is not offered by the existing software retrieval systems. [ABSTRACT FROM AUTHOR] |
| Copyright of Neural Computing & Applications 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 |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 4689399 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A Visualised Software Library: Nested Self-Organising Maps for Retrieving and Browsing Reusable Software Assets. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Ye%2C+H%2E%22">Ye, H.</searchLink><br /><searchLink fieldCode="AR" term="%22Lo%2C+B%2E+W%2E+N%2E%22">Lo, B. W. N.</searchLink> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Neural+Computing+%26+Applications%22">Neural Computing & Applications</searchLink>. 2000, Vol. 9 Issue 4, p266. 14p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Computer+software%22">Computer software</searchLink><br /><searchLink fieldCode="DE" term="%22Self-organizing+maps%22">Self-organizing maps</searchLink><br /><searchLink fieldCode="DE" term="%22Self-organizing+systems%22">Self-organizing systems</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This paper presents an approach to self-structuring software libraries. The authors developed a representation scheme to construct a feature space over a collection of software assets. The feature space is represented and classified by a variety of the self-organising map, called the Nested Software Self-Organising Map (NSSOM), consisting of a top map and a set of sub-maps nested in the top map. The clustering on the top map provides general improvements in retrieval recall, while the lower-level nested maps further elaborate the clusters' into more specific groups enhancing retrieval precision. The results of preliminary evaluation showed that NSSOM is capable of enhancing precision without sacrificing recall. In addition, a user-friendly browsing facility has also been developed which helps users predict the desired components by providing an intelligible search space. The present approach attempts to achieve an optimal combination of efficiency, accuracy and user-friendliness, which is not offered by the existing software retrieval systems. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Neural Computing & Applications 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.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=4689399 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s005210070004 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 14 StartPage: 266 Subjects: – SubjectFull: Computer software Type: general – SubjectFull: Self-organizing maps Type: general – SubjectFull: Self-organizing systems Type: general Titles: – TitleFull: A Visualised Software Library: Nested Self-Organising Maps for Retrieving and Browsing Reusable Software Assets. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Ye, H. – PersonEntity: Name: NameFull: Lo, B. W. N. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 10 Text: 2000 Type: published Y: 2000 Identifiers: – Type: issn-print Value: 09410643 Numbering: – Type: volume Value: 9 – Type: issue Value: 4 Titles: – TitleFull: Neural Computing & Applications Type: main |
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