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100   $a20130712d2013      u|         0
101 0 $aeng
135   $aurnn|008mamaa
181   $ctxt
182   $cc
183   $acr
200 10$aEarly Software Reliability Prediction $eA Fuzzy Logic Approach /$fby Ajeet Kumar Pandey, Neeraj Kumar Goyal
205   $a1st ed. 2013.
210  1$aNew Delhi :$cSpringer India :$cImprint: Springer,$d2013.
215   $a1 online resource (XIX, 153 p.) 
225 1 $aStudies in Fuzziness and Soft Computing,$x1434-9922 ;$v303
300   $aBibliographic Level Mode of Issuance: Monograph
311 08$a9788132211754 
311 08$a8132211758 
320   $aIncludes bibliographical references and index.
327   $aIntroduction -- 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.
330   $aThe development of software system with acceptable level of reliability and quality within available time frame and budget becomes a challenging objective. This objective could be achieved to some extent through early prediction of number of faults present in the software, which reduces the cost of development as it provides an opportunity to make early corrections during development process. The book presents an early software reliability prediction model that will help to grow the reliability of the software systems by monitoring it in each development phase, i.e. from requirement phase to testing phase. Different approaches are discussed in this book to tackle this challenging issue. An important approach presented in this book is a model to classify the modules into two categories (a) fault-prone and (b) not fault-prone. The methods presented in this book for assessing expected number of faults present in the software, assessing expected number of faults present at the end of each phase and classification of software modules in fault-prone or no fault-prone category are easy to understand, develop and use for any practitioner. The practitioners are expected to gain more information about their development process and product reliability, which can help to optimize the resources used.
410  0$aStudies in Fuzziness and Soft Computing,$x1434-9922 ;$v303
606   $aComputational intelligence
606   $aSoftware engineering
606   $aStatistics
606   $aComputational Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/T11014
606   $aSoftware Engineering$3https://scigraph.springernature.com/ontologies/product-market-codes/I14029
606   $aStatistics and Computing/Statistics Programs$3https://scigraph.springernature.com/ontologies/product-market-codes/S12008
615  0$aComputational intelligence.
615  0$aSoftware engineering.
615  0$aStatistics.
615 14$aComputational Intelligence.
615 24$aSoftware Engineering.
615 24$aStatistics and Computing/Statistics Programs.
676   $a006.3
700   $aPandey$b Ajeet Kumar$4aut$4http://id.loc.gov/vocabulary/relators/aut$01424433
702   $aGoyal$b Neeraj Kumar$4aut$4http://id.loc.gov/vocabulary/relators/aut
906   $aBOOK
912   $a9910741141103321
996   $aEarly Software Reliability Prediction$93553612
997   $aUNINA