A generalized multiple environmental factors software reliability model with stochastic fault detection process

Software systems have been widely applied in numerous safety–critical domains; however, large-scale software development is still considered as a complicated and expensive activity. As the latest trends in software industry accelerate the complexity and dependency of software development, such compl...

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Bibliographic Details
Published in:Annals of operations research Vol. 311; no. 1; pp. 525 - 546
Main Authors: Zhu, Mengmeng, Pham, Hoang
Format: Journal Article
Language:English
Published: New York Springer US 01.04.2022
Springer
Springer Nature B.V
Subjects:
Business and Management
Combinatorics
Fault detection
Fault location (Engineering)
Growth models
Martingales
Methods
Operations research
Operations Research/Decision Theory
Randomness
Reliability (Engineering)
Reliability analysis
Reliability aspects
S.I. : Reliability Modeling with Applications Based on Big Data
Software development
Software engineering
Software reliability
Theory of Computation
United States
Software reliability growth model
Martingale framework
Environmental factors
ISSN:0254-5330, 1572-9338
Online Access:Get full text
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Summary:Software systems have been widely applied in numerous safety–critical domains; however, large-scale software development is still considered as a complicated and expensive activity. As the latest trends in software industry accelerate the complexity and dependency of software development, such complicated and human-centered process needs to be addressed well. Meanwhile, recent survey investigations (Zhu et al. in J Syst Softw 109:150–160, 2015; Zhu and Pham in J Syst Softw 132:72–84, 2017) revealed that environmental factors, defined from software development, have significant impacts on software reliability. Considering such significant impacts, we first propose a generalized multiple-environmental-factors software reliability growth model with multiple environmental factors and the associated randomness under the martingale framework. The randomness is reflected on the process of detecting software fault. Indeed, this is a stochastic fault detection process. As an illustration, a specific multiple-environmental-factors software reliability growth model incorporating two specific environmental factors, percentage of reused modules and frequency of program specification change , is further developed. Lastly, we employ two real-world data sets to demonstrate the prediction performance of the proposed generalized multiple-environmental-factors software reliability growth model.
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ISSN:0254-5330
1572-9338
DOI:10.1007/s10479-020-03732-3