The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Analysis and Comparison of Neural Network Models for Software Development Effort Estimation
|
|
Author(s): Kamlesh Dutta (Department of CSE, National Institute of Technology, Hamirpur, India), Varun Gupta (Amity School of Engineering and Technology, Amity University, Noida, India)and Vachik S. Dave (Department of CSE, National Institute of Technology, Hamirpur, India)
Copyright: 2022
Pages: 29
Source title:
Research Anthology on Agile Software, Software Development, and Testing
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-6684-3702-5.ch009
Purchase
|
Abstract
Prediction of software development is the key task for the effective management of any software industry. The accuracy and reliability of the prediction mechanisms used for the estimation of software development effort is also important. A series of experiments are conducted to gradually progress towards the improved accurate estimation of the software development effort. However, while conducting these experiments, it was found that the size of the training set was not sufficient to train a large and complex artificial neural network (ANN). To overcome the problem of the size of the available training data set, a novel multilayered architecture based on a neural network model is proposed. The accuracy of the proposed multi-layered model is assessed using different criteria, which proves the pre-eminence of the proposed model.
Related Content
|
Subhadip Kowar, Sneha Mukherjee, Shramana Ghosh.
© 2025.
26 pages.
|
|
C. V. Suresh Babu, Mala Raja Sekhar, A. Sachin, Bala Brindha.
© 2025.
26 pages.
|
|
A. D. N. Sarma.
© 2025.
32 pages.
|
|
Muhammad Usman Tariq.
© 2025.
26 pages.
|
|
Maaike Stoops, Pablo Alfonso Aguilar Calderón, Óscar Manuel Peña Bañuelos.
© 2025.
30 pages.
|
|
Pablo Alfonso Aguilar Calderón, José Alfonso Aguilar-Calderón, Dominik Morales-Silva, Carolina Tripp-Barba, Pedro Alfonso Aguilar-Calderón, Aníbal Zaldívar-Colado, Oscar Manuel Peña-Bañuelos.
© 2025.
30 pages.
|
|
Carlos Villarrubia, David Granada, Juan Manuel Vara.
© 2025.
34 pages.
|
|
|