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010   $a3-031-64803-X
024 7 $a10.1007/978-3-031-64803-8
035   $a(MiAaPQ)EBC31691114
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035   $a(CKB)36213718900041
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035   $a(Au-PeEL)EBL31691828
035   $a(DE-He213)978-3-031-64803-8
035   $a(EXLCZ)9936213718900041
100   $a20240925d2024      u|         0
101 0 $aeng
135   $aurcnu||||||||
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aApplied OSS Reliability Assessment Modeling, AI and Tools $eMathematics and AI for OSS Reliability Assessment /$fby Yoshinobu Tamura, Shigeru Yamada
205   $a1st ed. 2024.
210  1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2024.
215   $a1 online resource (191 pages)
225 1 $aSpringer Series in Reliability Engineering,$x2196-999X
311 08$a3-031-64802-1 
327   $aOpen Source Software Reliability -- Stochastic Differential Equation Model for OSS Reliability Analysis -- Dimensional Stochastic Differential Equation Model for OSS Reliability Analysis -- Jump Diffusion Process Model for OSS Reliability Analysis -- Cyclically Two Dimensional Stochastic Differential Equation Modeling -- Cyclically Two Dimensional Jump Diffusion Process Modeling -- Three Dimensional Tool Based on Noisy Model -- Deep Learning Method Based on fault big data Analysis for OSS Reliability Assessment -- Deep Learning Approach for OSS Reliability Assessment Considering Wiener Process -- Deep Learning Approach for OSS Reliability Assessment Considering Jump Diffusion Process -- Performance Illustrations of the Developed Application Tool Based on Deep Learning -- Exercise.
330   $aThis textbook introduces the theory and application of open source software (OSS) reliability. The measurement and management of open source software are essential to produce and maintain quality and reliable systems while using open source software. This book describes the latest methods for the reliability assessment of open source software. It presents the state of the art of open source software reliability measurement and assessment based on stochastic modeling and deep learning approaches. It introduces several stochastic reliability analyses of OSS computing with application along with actual OSS project data. The book contains exercises to aid learning and is useful for graduate students and researchers.
410  0$aSpringer Series in Reliability Engineering,$x2196-999X
606   $aOpen source software
606   $aArtificial intelligence
606   $aCooperating objects (Computer systems)
606   $aIndustrial engineering
606   $aProduction engineering
606   $aData protection
606   $aComputers
606   $aOpen Source
606   $aArtificial Intelligence
606   $aCyber-Physical Systems
606   $aIndustrial and Production Engineering
606   $aData and Information Security
606   $aHardware Performance and Reliability
615  0$aOpen source software.
615  0$aArtificial intelligence.
615  0$aCooperating objects (Computer systems)
615  0$aIndustrial engineering.
615  0$aProduction engineering.
615  0$aData protection.
615  0$aComputers.
615 14$aOpen Source.
615 24$aArtificial Intelligence.
615 24$aCyber-Physical Systems.
615 24$aIndustrial and Production Engineering.
615 24$aData and Information Security.
615 24$aHardware Performance and Reliability.
676   $a006.3
700   $aTamura$b Yoshinobu$f1950-$01846302
702   $aYamada$b Shigeru
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
906   $aBOOK
912   $a9910890186103321
996   $aApplied OSS Reliability Assessment Modeling, AI and Tools$94430525
997   $aUNINA