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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Vydané v:	The Journal of supercomputing Ročník 80; číslo 11; s. 16173 - 16201
Hlavní autori:	Bahnam, Baydaa Sulaiman, Dawwod, Suhair Abd, Younis, Mohammed Chachan
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York Springer US 01.07.2024 Springer Nature B.V
Predmet:	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
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Abstract	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.
AbstractList	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.
Author	Dawwod, Suhair Abd Younis, Mohammed Chachan Bahnam, Baydaa Sulaiman
Author_xml	– sequence: 1 givenname: Baydaa Sulaiman surname: Bahnam fullname: Bahnam, Baydaa Sulaiman email: baydaa_sulaiman@uomosul.edu.iq organization: Department of Software, College of Computer Sciences and Mathematics, University of Mosul – sequence: 2 givenname: Suhair Abd surname: Dawwod fullname: Dawwod, Suhair Abd organization: Department Management Information Systems, College of Administration and Economics, University of Mosul – sequence: 3 givenname: Mohammed Chachan surname: Younis fullname: Younis, Mohammed Chachan organization: Department of Computer Sciences, College of Computer Sciences and Mathematics, University of Mosul
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Keywords	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
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Snippet	In artificial intelligence (AI), optimization techniques are used to solve several problems in different fields. One of these areas is software reliability...
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SubjectTerms	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
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Title	Optimizing software reliability growth models through simulated annealing algorithm: parameters estimation and performance analysis
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