Optimizing software reliability growth models through simulated annealing algorithm: parameters estimation and performance analysis

In artificial intelligence (AI), optimization techniques are used to solve several problems in different fields. One of these areas is software reliability verification, which is an important part of software products, as it helps determine how reliable the software is to complete its functions. Thi...

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Bibliographic Details
Published in:The Journal of supercomputing Vol. 80; no. 11; pp. 16173 - 16201
Main Authors: Bahnam, Baydaa Sulaiman, Dawwod, Suhair Abd, Younis, Mohammed Chachan
Format: Journal Article
Language:English
Published: New York Springer US 01.07.2024
Springer Nature B.V
Subjects:
Artificial intelligence
Compilers
Computer Science
Failure
Growth models
Interpreters
Optimization algorithms
Parameter estimation
Particle swarm optimization
Processor Architectures
Programming Languages
Search algorithms
Simulated annealing
Software reliability
Delayed S-shaped model
Simulated annealing algorithm (SA)
Artificial intelligence (AI)
Power model
Musa-Okumoto logarithmic model
Swarm intelligence (SI)
Software reliability growth models (SRGMs)
Parameters estimation
Comparative analysis
ISSN:0920-8542, 1573-0484
Online Access:Get full text
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Summary:In artificial intelligence (AI), optimization techniques are used to solve several problems in different fields. One of these areas is software reliability verification, which is an important part of software products, as it helps determine how reliable the software is to complete its functions. This is done by estimating the parameters of software reliability growth models (SRGMs). SRGMs predict the expected number of failures after completion, while also serving as an indicator of software readiness for delivery. Therefore, this study aims to optimize the estimation of these parameters based on the available failure data using one of the stochastic optimization algorithms, the simulated annealing algorithm (SA) due to its power and effectiveness. Three SRGMs’ models are studied: delayed S-shaped, Musa-Okumoto logarithmic and Power models, to examine the feasibility of the proposed algorithm using five different data sets. The results were compared and analyzed with several algorithms: Particle swarm optimization (PSO), cuckoo search (CS), modify whale optimization algorithm (MWOA), S-shaped model with logistic TEF and social spider algorithm (SSA). A comparison was also made with recent SRGMs that do not rely on AI techniques. The results showed that the proposed algorithm based on SA outperformed all other methods in finding the optimal parameters.
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ISSN:0920-8542
1573-0484
DOI:10.1007/s11227-024-06046-4