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020 _a9783031648038
_9978-3-031-64803-8
024 7 _a10.1007/978-3-031-64803-8
_2doi
050 4 _aQA76.76.O62
072 7 _aU
_2bicssc
072 7 _aCOM051390
_2bisacsh
072 7 _aULJ
_2thema
100 1 _aTamura, Yoshinobu.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_984954
245 1 0 _aApplied OSS Reliability Assessment Modeling, AI and Tools
_h[electronic resource] :
_bMathematics and AI for OSS Reliability Assessment /
_cby Yoshinobu Tamura, Shigeru Yamada.
250 _a1st ed. 2024.
264 1 _aCham :
_bSpringer Nature Switzerland :
_bImprint: Springer,
_c2024.
300 _aXI, 188 p. 99 illus., 80 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSpringer Series in Reliability Engineering,
_x2196-999X
505 0 _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.
520 _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.
650 0 _aOpen source software.
_99313
650 0 _aArtificial intelligence.
_958441
650 0 _aCooperating objects (Computer systems).
_964303
650 0 _aIndustrial engineering.
650 0 _aProduction engineering.
650 0 _aData protection.
650 0 _aComputers.
_965793
650 1 4 _aOpen Source.
_966214
650 2 4 _aArtificial Intelligence.
_958441
650 2 4 _aCyber-Physical Systems.
_965638
650 2 4 _aIndustrial and Production Engineering.
650 2 4 _aData and Information Security.
_966492
650 2 4 _aHardware Performance and Reliability.
_971397
700 1 _aYamada, Shigeru.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_984955
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031648021
776 0 8 _iPrinted edition:
_z9783031648045
776 0 8 _iPrinted edition:
_z9783031648052
830 0 _aSpringer Series in Reliability Engineering,
_x2196-999X
_971674
856 4 0 _uhttps://doi.org/10.1007/978-3-031-64803-8
912 _aZDB-2-ENG
912 _aZDB-2-SXE
999 _c63682
_d63682