Software reliability modeling. Fundamentals and applications

DOI10.1007/978-4-431-54565-1zbMath1275.62017OpenAlexW2247689863MaRDI QIDQ382496

Shigeru Yamada

Publication date: 20 November 2013

Published in: SpringerBriefs in Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/978-4-431-54565-1

Mathematics Subject Classification ID

Applications of statistics in engineering and industry; control charts (62P30) Introductory exposition (textbooks, tutorial papers, etc.) pertaining to statistics (62-01)

Related Items (14)

SOFTWARE RELIABILITY BASED ON RENEWAL PROCESS MODELING FOR ERROR OCCURRENCE DUE TO EACH BUG WITH PERIODIC DEBUGGING SCHEDULE ⋮ Deep Learning Approach Based on Fault Correction Time for Reliability Assessment of Cloud and Edge Open Source Software ⋮ NHPP-based change-point modeling for software reliability assessment and its application to software development management ⋮ Reliability computing and management considering the network traffic for a cloud computing ⋮ Bivariate change-point modeling for software reliability assessment with uncertainty of testing-environment factor ⋮ Software reliability modeling based on NHPP for error occurrence in each fault with periodic debugging schedule ⋮ An Ideal Software Release Policy for an Improved Software Reliability Growth Model Incorporating Imperfect Debugging with Fault Removal Efficiency and Change Point ⋮ Monotonic decrease of upper limit estimated with Gompertz model for data described using logistic model ⋮ Parameter optimization of software reliability models using improved differential evolution algorithm ⋮ Property of logistic data exposed with Gompertz model and resistance to noise in actual data ⋮ Properties of Gompertz data revealed with non-Gompertz integrable difference equation ⋮ A new software reliability growth model: multigeneration faults and a power-law testing-effort function ⋮ Model selection among growth curve models that have the same number of parameters ⋮ Forecasting with full use of data without interpolation on logistic curve model with missing data

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