IRMA-International.org: Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

Software Performance Estimate using Fuzzy Based Backpropagation Learning

Software Performance Estimate using Fuzzy Based Backpropagation Learning
View Sample PDF
Author(s): Khaleel Ahmad (Maulana Azad National Urdu University, India), Gaurav Kumar (Swami Vivekananda Subharti University, India), Abdul Wahid (Maulana Azad National Urdu University, India)and Mudasir M. Kirmani (Maulana Azad National Urdu University, India)
Copyright: 2016
Pages: 23
Source title: Psychology and Mental Health: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-5225-0159-6.ch015

Purchase

View Software Performance Estimate using Fuzzy Based Backpropagation Learning on the publisher's website for pricing and purchasing information.

Abstract

Accurate estimation of the software performance and its reliability is an important task in designing, developing and implementing software as per the desired requirements. With the increase in individuals relying on software application in their daily lives has resulted in increase in demand for good quality software with efficient performance. The professionals in the software industry are facing an uphill task of developing software with efficient performance measure and at the same time capable of evaluating software performance. In order to evaluate software performance it is necessary to have a method to estimate the software performance. The estimation of software performance plays an important role in predicting acceptability and longevity of a software product. Software performance estimation is essential in existing software-dominated environment where part of daily life is directly or indirectly dependent on software for fulfilling requirements. In this chapter discusses the reasons underlying the proposals and shows the pitfalls associated to these software attributes.

Related Content

Peter Arthur Barone. © 2023. 17 pages.
Patricia A. Goforth. © 2023. 22 pages.
Steven Lloyd Leeper. © 2023. 18 pages.
Neslihan Yayla. © 2023. 25 pages.
İlknur Gümüş. © 2023. 14 pages.
Sarah E. Daly. © 2023. 15 pages.
Yakup Alper Varış. © 2023. 22 pages.
Body Bottom