The Compatibility of Parametric Software Reliability Growth Models in PRGA
الموضوعات :
Reza Roshani
1
(
Department of Computer Engineering, Sari Branch, Islamic Azad University, Sari, Iran
)
Homayun Motameni
2
(
Department of Computer Engineering, Sari Branch, Islamic Azad University, Sari, Iran
)
Hosein Mohamadi
3
(
Department of Computer Engineering, Azadshar Branch, Islamic Azad University, Azadshar, Iran
)
الکلمات المفتاحية: Genetic Algorithm, compatibility, software reliability, SR Growth Model,
ملخص المقالة :
Software Reliability (SR) is a key non-operational feature measured when evaluating software quality. To enhance this feature, it is important to detect failures and mitigate them in the testing phase. SR can be increased by identifying and removing this failure from the defect data. The existing literature consists of many models/methods applicable to measuring SR, including the SR Growth Models (SRGMs). Generally, SRGMs are in two main types: parametric and non-parametric. As these models are diverse, when applying to a certain problem, the particular requirements and conditions of that problem should be taken into account. The current paper explains the fundamental concepts of reliability, then reviews the Parametric SR Growth Models (PSRGMs) and evaluates various approaches already proposed in this domain. In addition, this study investigates the SRGMs compatibility by means of a novel Parallel Real-valued Genetic Algorithm (PRGA)-based method. The results achieved under a variety of conditions for each model showed the extent of compatibility with GA.
[1] David D Hanagal and Nileema N Bhalerao, Software Reliability Growth Models, Springer, Singapore, 2021.
[2] Yeu-Shiang Huang, Kuei-Chen Chiu and Wan-Ming Chen, “A software reliability growth model for imperfect debugging,” Journal of Systems and Software, Vol.188:111267(1)-111267(15), 2022.https://doi.org/10.1016/j.jss.2022.111267
[3] Ajeet Kumar Pandey and Neeraj Kumar Goyal, Early software reliability prediction, Springer, 2015.
[4] Shigeru Yamada, Software reliability modeling: fundamentals and applications, Vol.5, Springer, 2014.
[5] Mirmorsal Madani, Homayun Motameni and Reza Roshani, “Fake News Detection Using Feature Extraction, Natural Language Processing, Curriculum Learning, and Deep Learning,” International Journal of Information Technology & Decision Making, 2023.
https://doi.org/10.1142/S0219622023500347
[6] Hoang Pham, System software reliability. Springer Science & Business Media, Springer London, 2007.
[7] Md Asraful Haque and Nesar Ahmad, “An effective software reliability growth model,” Safety and Reliability, Vol.40, No.4:209-220, 2021.https://doi.org/10.1080/09617353.2021.1921547
[8] M. Zubair Ahmad and N. Ahmad,“Parametric Software Reliability Growth Model with Testing Effort: A Review,”in2021 International Conference on Computational Performance Evaluation (ComPE). Shillong, India, 2021. https://doi.org/10.1109/ComPE53109.2021.9752232
[9] Fabio Q. B. da Silva, André L. M. Santos, Sérgio Soares, A. César C. França, Cleviton V. F. Monteiro and Felipe Farias Maciel, “Six years of systematic literature reviews in software engineering: An updated tertiary study,” Information and Software Technology, Vol.53, No.9:899-913, 2011.
https:/doi.org/10.1016/j.infsof.2011.04.004
[10] Felipe Febrero, Coral Calero, and M Ángeles Moraga, “A Systematic Mapping Study of Software Reliability Modeling,” Information and Software Technology, Vol.56, No.8:839-849, 2014.https:/doi.org/10.1016/j.infsof.2014.03.006
[11] Ajeet Kumar Pandey and N. Goyal,“Early Software Reliability Prediction - A Fuzzy Logic Approach,” in Studies in Fuzziness and Soft Computing, 2013.
[12] E. Kashyap and A. Rana,“A Comparative Study of S-shape and Concave Software Reliability Growth Models,”in2015 International Conference on Computational Intelligence and Communication Networks (CICN), 2015, pp.1452-1455. https://doi.org/10.1109/CICN.2015.280
[13] Kezhong Lu and Zongmin Ma, “A modified whale optimization algorithm for parameter estimation of software reliability growth models,” Journal of Algorithms & Computational Technology, Vol.15:1-14, 2021.
https://doi.org/10.1177/17483026211034442
[14] Neha Miglani, Poonam Rana, and M. Scholar, “Ranking of Software Reliability Growth Models using Greedy Approach,”Global Journal of Business Management and Information Technology, Vol.1, No.2:119-124, 2011.
[15] Ritu Bibyan and Sameer Anand, “Ranking of Multi-release Software Reliability Growth Model Using Weighted Distance-Based Approach,”inOptimization Models in Software Reliability, Springer International Publishing, Cham, 2022, pp.355-373.
https://doi.org/10.1007/978-3-030-78919-0_16
[16] Cheng-Gang Bai, “Bayesian network based software reliability prediction with an operational profilem,” Journal of Systems and Software, Vol.77, No.2:103-112, 2005.
https:/doi.org/10.1016/j.jss.2004.11.034
[17] M. Geetha B, Santhosh Kumar Rand Cristin T. Daniya, “Least Square Estimation of Parameters for Linear Regression,” International Journal of Control and Automation, Vol.13, No.02:447-452, 2020.
[18] In Jae Myung, “Tutorial on maximum likelihood estimation,” Journal of Mathematical Psychology, Vol.47, No.1:90-100, 2003.
https:/doi.org/10.1016/S0022-2496(02)00028-7
[19] C. Ojeda, W. Palma, S. Eyheramendy, and F. Elorrieta, “Extending time-series models for irregular observational gaps with a moving average structure for astronomical sequences,” RAS Techniques and Instruments, Vol.2, No.1:33-44, 2023.https://doi.org/10.1093/rasti/rzac011
[20] SMK Quadri and N Ahmad, “Software reliability growth modeling with new modified weibull testing–effort and optimal release policy,” International Journal of Computer Applications, Vol.6, No.12:1-10, 2010.
[21] Cong Jin and Shu-Wei Jin, “Parameter optimization of software reliability growth model with S-shaped testing-effort function using improved swarm intelligent optimization,” Applied Soft Computing, Vol.40:283-291, 2016.
https:/doi.org/10.1016/j.asoc.2015.11.041
[22] Md A Haque and Nesar Ahmad, “Key issues in software reliability growth models,” Recent Advances in Computer Science and Communications (Formerly: Recent Patents on Computer Science), Vol.15, No.5:741-747, 2022.https://doi.org/10.2174/2666255813999201012182821
[23] Amrit L Goel and Kazu Okumoto, “Time-dependent error-detection rate model for software reliability and other performance measures,” IEEE transactions on Reliability, Vol.28, No.3:206-211, 1979.https://doi.org/10.1109/TR.1979.5220566
[24] Shigeru Yamada, Mitsuru Ohba, and Shunji Osaki, “S-shaped reliability growth modeling for software error detection,” IEEE Transactions on reliability, Vol.32, No.5:475-484, 1983.
[25] A. L. Goel, “Software Reliability Models: Assumptions, Limitations, and Applicability,” IEEE Transactions on Software Engineering, Vol. SE-11, No.12:1411-1423, 1985.
https://doi.org/10.1109/TSE.1985.232177
[26] Mitsuru Ohba, Inflection S-shaped software reliability growth model, in Stochastic models in reliability theory, Springer: Berlin, Heidelberg, 1984, pp.144-162.
https://doi.org/10.1007/978-3-642-45587-2_10
[27] PK Kapur, DN Goswami, Amit Bardhan, and Ompal Singh, “Flexible software reliability growth model with testing effort dependent learning process,” Applied Mathematical Modelling, Vol.32, No.7:1298-1307, 2008.
https://doi.org/10.1016/j.apm.2007.04.002
[28] Hoang Pham and Xuemei Zhang, “An NHPP software reliability model and its comparison,” International Journal of Reliability, Quality and Safety Engineering, Vol.4, No.03:269-282, 1997.
[29] Fan Li and Ze-Long Yi, “A new software reliability growth model: multigeneration faults and a power-law testing-effort function,” Mathematical Problems in Engineering, Vol.2016:1-13, 2016.
https://doi.org/10.1155/2016/9276093
[30] Satish Kumar and Harish, “Estimate the Software Reliability with Recursive Go-Model,” International Journal of Research in Computer Applications and Information Technology (IJRCAIT), Vol.3, No.6:40-49, 2015.
[31] Taehyoun Kim, Kwangkyu Lee, and Jongmoon Baik, “An effective approach to estimating the parameters of software reliability growth models using a real-valued genetic algorithm,” Journal of Systems and Software, Vol.102:134-144, 2015.https://doi.org/10.1016/j.jss.2015.01.001
[32] Mirmorsal Madani, Homayun Motameni, and Hosein Mohamadi, “KNNGAN: an oversampling technique for textual imbalanced datasets,” The Journal of Supercomputing, Vol.79, No.5:5291-5326, 2023.
https://doi.org/10.1007/s11227-022-04851-3
[33] Subodh Kumar, Introduction to parallel programming, Cambridge University Press, 2022.
[34] Reza Roshani, Homayon Motameni, and Hosein Mohamadi, “A decentralized method for initial populations of genetic algorithms,” The Journal of Supercomputing:1-20 , 2023.
https://doi.org/10.1007/s11227-023-05066-w
[35] Reza Roshani and Mohammad Karim Sohrabi, “Parallel Genetic Algorithm for Shortest Path Routing Problem with Collaborative Neighbors,” Ciência e Natura, Vol. 37, No.0:327-333, 2015.https://doi.org/10.5902/2179460X20790
[36] Madani Mirmorsal, “Comparing learning algorithms in neural network for diagnosing cardiovascular disease,”CoRR, Vol.abs/1611.01678, 2016.
https://doi.org/10.48550/arXiv.1611.01678
[37] Bejjam Vasundhara Devi and R. Kanniga Devi, “Software reliability models based on machine learning techniques: A review,”inAIP Conference Proceedings, Vol.2463, No.1, 2022, pp.020038.
https://doi.org/10.1063/5.0080442
[38] Da H. Lee, In H. Chang, and Hoang Pham “Software Reliability Growth Model with Dependent Failures and Uncertain Operating Environments,”Applied Sciences,Vol.12, No.23:12383(1)-12383(14), 2022.
https://doi.org/10.3390/app122312383
[39] Mohd Anjum, Md. Asraful Haque, and Nesar Ahmad, “Analysis and Ranking of Software Reliability Models Based on Weighted Criteria Value,” International Journal of Information Technology and Computer Science, Vol.5:1-14, 2013.
[40] N. Ullah, M. Morisio, and A. Vetro,“A Comparative Analysis of Software Reliability Growth Models using Defects Data of Closed and Open Source Software,”in2012 35th Annual IEEE Software Engineering Workshop, Heraklion, Greece, 2012, pp.187-192.
https://doi.org/10.1109/SEW.2012.26
