Academic Journal
A hybrid model for prediction of software effort based on team size.
| Τίτλος: | A hybrid model for prediction of software effort based on team size. |
|---|---|
| Συγγραφείς: | Rai, Prerana, Verma, Dinesh Kumar, Kumar, Shishir |
| Πηγή: | IET Software (Wiley-Blackwell); Dec2021, Vol. 15 Issue 6, p365-375, 11p |
| Θεματικοί όροι: | Computer software management, Computer software development, Computer programming management, Support vector machines, Regression analysis |
| Περίληψη: | Most of the software development organisations frequently use an appreciable amount of resources to estimate the effort in the beginning of the development process. In most of the cases, inaccurate estimates tend to wastage of these resources. Very few generalised models have been found in the literature. These models have been developed using the prototype dataset of the organisation. The project management team of an organisation tries to predict the effort needed for the development of software using various mathematical techniques. These techniques are mostly based on statistical methods (viz. simple linear regression (SLR), multi linear regression, support vector machine, cascade correlation neural network (CCNN) etc.) and some probability‐based models. They use historical data of similar projects. The work presented in this article envisages the use of Support Vector Regression (SVR) and constructive cost model (COCOMO), where SVR can be used for both linear and non‐linear models and COCOMO can be used as a regression model. The proposed hybrid model has been tested on the International Software Benchmarking Standards Group dataset. The data has been grouped according to the size of man power. It has been found that the proposed model yields better results than the SVR or SLR for each group of data in general. [ABSTRACT FROM AUTHOR] |
| Copyright of IET Software (Wiley-Blackwell) 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.) | |
| Βάση Δεδομένων: | Complementary Index |
| FullText | Links: – Type: other Text: Availability: 0 |
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| Header | DbId: edb DbLabel: Complementary Index An: 154046501 RelevancyScore: 916 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 915.657653808594 |
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| Items | – Name: Title Label: Title Group: Ti Data: A hybrid model for prediction of software effort based on team size. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Rai%2C+Prerana%22">Rai, Prerana</searchLink><br /><searchLink fieldCode="AR" term="%22Verma%2C+Dinesh+Kumar%22">Verma, Dinesh Kumar</searchLink><br /><searchLink fieldCode="AR" term="%22Kumar%2C+Shishir%22">Kumar, Shishir</searchLink> – Name: TitleSource Label: Source Group: Src Data: IET Software (Wiley-Blackwell); Dec2021, Vol. 15 Issue 6, p365-375, 11p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Computer+software+management%22">Computer software management</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+software+development%22">Computer software development</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+programming+management%22">Computer programming management</searchLink><br /><searchLink fieldCode="DE" term="%22Support+vector+machines%22">Support vector machines</searchLink><br /><searchLink fieldCode="DE" term="%22Regression+analysis%22">Regression analysis</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Most of the software development organisations frequently use an appreciable amount of resources to estimate the effort in the beginning of the development process. In most of the cases, inaccurate estimates tend to wastage of these resources. Very few generalised models have been found in the literature. These models have been developed using the prototype dataset of the organisation. The project management team of an organisation tries to predict the effort needed for the development of software using various mathematical techniques. These techniques are mostly based on statistical methods (viz. simple linear regression (SLR), multi linear regression, support vector machine, cascade correlation neural network (CCNN) etc.) and some probability‐based models. They use historical data of similar projects. The work presented in this article envisages the use of Support Vector Regression (SVR) and constructive cost model (COCOMO), where SVR can be used for both linear and non‐linear models and COCOMO can be used as a regression model. The proposed hybrid model has been tested on the International Software Benchmarking Standards Group dataset. The data has been grouped according to the size of man power. It has been found that the proposed model yields better results than the SVR or SLR for each group of data in general. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of IET Software (Wiley-Blackwell) 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.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1049/sfw2.12048 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 11 StartPage: 365 Subjects: – SubjectFull: Computer software management Type: general – SubjectFull: Computer software development Type: general – SubjectFull: Computer programming management Type: general – SubjectFull: Support vector machines Type: general – SubjectFull: Regression analysis Type: general Titles: – TitleFull: A hybrid model for prediction of software effort based on team size. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Rai, Prerana – PersonEntity: Name: NameFull: Verma, Dinesh Kumar – PersonEntity: Name: NameFull: Kumar, Shishir IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 12 Text: Dec2021 Type: published Y: 2021 Identifiers: – Type: issn-print Value: 17518806 Numbering: – Type: volume Value: 15 – Type: issue Value: 6 Titles: – TitleFull: IET Software (Wiley-Blackwell) Type: main |
| ResultId | 1 |