A hybrid model for prediction of software effort based on team size
Abstract 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...
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Wiley
2021-12-01
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| Series: | IET Software |
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| Online Access: | https://doi.org/10.1049/sfw2.12048 |
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| author | Prerana Rai Dinesh Kumar Verma Shishir Kumar |
| author_facet | Prerana Rai Dinesh Kumar Verma Shishir Kumar |
| author_sort | Prerana Rai |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-c885e59319114702b603b1f867971ac9 |
| institution | Kabale University |
| issn | 1751-8806 1751-8814 |
| language | English |
| publishDate | 2021-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Software |
| spelling | doaj-art-c885e59319114702b603b1f867971ac92025-02-03T01:29:44ZengWileyIET Software1751-88061751-88142021-12-0115636537510.1049/sfw2.12048A hybrid model for prediction of software effort based on team sizePrerana Rai0Dinesh Kumar Verma1Shishir Kumar2Department of Computer Science and Engineering Jaypee University of Engineering and Technology Guna IndiaDepartment of Computer Science and Engineering Jaypee University of Engineering and Technology Guna IndiaSchool of Information Science & Technology Babasaheb Bhimrao Ambedkar University, (A Central University) Lucknow IndiaAbstract 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.https://doi.org/10.1049/sfw2.12048neural netsprobabilityproject managementregression analysissoftware cost estimationsoftware development management |
| spellingShingle | Prerana Rai Dinesh Kumar Verma Shishir Kumar A hybrid model for prediction of software effort based on team size IET Software neural nets probability project management regression analysis software cost estimation software development management |
| title | A hybrid model for prediction of software effort based on team size |
| title_full | A hybrid model for prediction of software effort based on team size |
| title_fullStr | A hybrid model for prediction of software effort based on team size |
| title_full_unstemmed | A hybrid model for prediction of software effort based on team size |
| title_short | A hybrid model for prediction of software effort based on team size |
| title_sort | hybrid model for prediction of software effort based on team size |
| topic | neural nets probability project management regression analysis software cost estimation software development management |
| url | https://doi.org/10.1049/sfw2.12048 |
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