TY  - BOOK
AU  - Tamura,Yoshinobu
AU  - Yamada,Shigeru
ED  - SpringerLink (Online service)
TI  - Applied OSS Reliability Assessment Modeling, AI and Tools: Mathematics and AI for OSS Reliability Assessment 
T2  - Springer Series in Reliability Engineering,
SN  - 9783031648038
AV  - QA76.76.O62 
U1  - 005.3 23
PY  - 2024///
CY  - Cham
PB  - Springer Nature Switzerland, Imprint: Springer
KW  - Open source software
KW  - Artificial intelligence
KW  - Cooperating objects (Computer systems)
KW  - Industrial engineering
KW  - Production engineering
KW  - Data protection
KW  - Computers
KW  - Open Source
KW  - Artificial Intelligence
KW  - Cyber-Physical Systems
KW  - Industrial and Production Engineering
KW  - Data and Information Security
KW  - Hardware Performance and Reliability
N1  - Open 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
N2  - This 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
UR  - http://libcon.rec.uabc.mx:2048/login?url=https://doi.org/10.1007/978-3-031-64803-8
ER  -