Early Software Reliability Prediction - A Fuzzy Logic Approach (Paperback, Softcover reprint of the original 1st ed. 2013)

The development of software system with acceptable level of reliability and quality within available time frame and budget becomes a challenging objective. This objective could be achieved to some extent through early prediction of number of faults present in the software, which reduces the cost of development as it provides an opportunity to make early corrections during development process. The book presents an early software reliability prediction model that will help to grow the reliability of the software systems by monitoring it in each development phase, i.e. from requirement phase to testing phase. Different approaches are discussed in this book to tackle this challenging issue. An important approach presented in this book is a model to classify the modules into two categories (a) fault-prone and (b) not fault-prone. The methods presented in this book for assessing expected number of faults present in the software, assessing expected number of faults present at the end of each phase and classification of software modules in fault-prone or no fault-prone category are easy to understand, develop and use for any practitioner. The practitioners are expected to gain more information about their development process and product reliability, which can help to optimize the resources used.

General

Imprint:	Springer, India, Private Ltd
Country of origin:	India
Series:	Studies in Fuzziness and Soft Computing, 303
Release date:	August 2015
First published:	2013
Authors:	Ajeet Kumar Pandey • Neeraj Kumar Goyal
Dimensions:	235 x 155 x 10mm (L x W x T)
Format:	Paperback
Pages:	153
Edition:	Softcover reprint of the original 1st ed. 2013
ISBN-13:	978-81-322-1742-8
Categories:	Books > Computing & IT > Computer programming > Software engineering Books > Computing & IT > Computer software packages > Other software packages > Mathematical & statistical software Books > Computing & IT > Applications of computing > Artificial intelligence > General
LSN:	81-322-1742-X
Barcode:	9788132217428

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