Early Software Reliability Prediction
Pandey, Ajeet Kumar.
Early Software Reliability Prediction A Fuzzy Logic Approach / [electronic resource] : by Ajeet Kumar Pandey, Neeraj Kumar Goyal. - XIX, 153 p. online resource. - Studies in Fuzziness and Soft Computing, 303 1434-9922 ; . - Studies in Fuzziness and Soft Computing, 303 .
Introduction -- Backgrounds: Software Quality and Reliability Prediction -- Early Fault Prediction using Software Metrics and Process Maturity -- Multistage Model for Residual Fault Prediction -- Prediction and Ranking of Fault-prone Software Modules -- Reliability Centric Test Case Prioritization -- Software Reliability and Operational Profile -- Appendices -- References.
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.
9788132211761
10.1007/978-81-322-1176-1 doi
Engineering.
Software engineering.
Statistics.
Computational intelligence.
Engineering.
Computational Intelligence.
Software Engineering.
Statistics and Computing/Statistics Programs.
Q342
006.3
Early Software Reliability Prediction A Fuzzy Logic Approach / [electronic resource] : by Ajeet Kumar Pandey, Neeraj Kumar Goyal. - XIX, 153 p. online resource. - Studies in Fuzziness and Soft Computing, 303 1434-9922 ; . - Studies in Fuzziness and Soft Computing, 303 .
Introduction -- Backgrounds: Software Quality and Reliability Prediction -- Early Fault Prediction using Software Metrics and Process Maturity -- Multistage Model for Residual Fault Prediction -- Prediction and Ranking of Fault-prone Software Modules -- Reliability Centric Test Case Prioritization -- Software Reliability and Operational Profile -- Appendices -- References.
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.
9788132211761
10.1007/978-81-322-1176-1 doi
Engineering.
Software engineering.
Statistics.
Computational intelligence.
Engineering.
Computational Intelligence.
Software Engineering.
Statistics and Computing/Statistics Programs.
Q342
006.3