Skip to main content
SpringerLink
Log in

An enhanced salp swarm optimizer boosted by local search algorithm for modelling prediction problems in software engineering

  • Published:
Artificial Intelligence Review Aims and scope Submit manuscript

Abstract

Scientific communities are still motivated to create novel approaches and methodologies for early estimation of software project development efforts and testing efforts in soft computing environments due to scheduling and budgetary concerns. Therefore, the software engineering prediction problems (SEPPs) are formulated as machine learning (ML) models with the aim of addressing these issues. In such methodologies that may exhibit significant limitations and drawbacks, efficient metaheuristic approaches are essential to improving prediction performance. Accordingly, this study aims to address software test effort prediction (STP) and software development effort prediction (SEP) with the aim of maximizing prediction accuracy, which in turn minimizes overall project costs and optimizes resource allocation. To achieve this goal, we developed several ML models composed of a backpropagation neural network (BPNN). The proposed models contain the Salp Swarm Algorithm (SSA), which is utilized to replace the traditional network training method and tackle its limitations. The models also contain the great deluge (GD) local search algorithm, which is hybridized with the SSA algorithm to enhance optimization capabilities by finding more balance between exploration and exploitation. During the validation stage of this study, fourteen benchmark datasets were utilized to evaluate the developed models for each of the respective problems. The obtained results were quantified using eight performance metrics and compared across two sections. In the first section, a comparison was made between the results of the hybrid-developed model (HSSA) and those of the standard SSA algorithm and BPNN. In the second comparison, the performance of the HSSA model was compared with several contemporary techniques that are considered state-of-the-art. The evaluation shows that the HSSA performs better than related approaches in most cases for both problems. Finally, additional analysis was performed on the collected results, including examinations of statistical significance, distribution through box plots, and model convergence behavior.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Notes

  1. https://figshare.com.

  2. https://zenodo.org/.

  3. https://www.openml.org.

  4. https://www.kaggle.com.

  5. http://promise.site.uottawa.ca/SERepository/datasets-page.html.

References

  • Abbassi A, Abbassi R, Heidari AA, Oliva D, Chen H, Habib A, Jemli M, Wang M (2020) Parameters identification of photovoltaic cell models using enhanced exploratory salp chains-based approach. Energy 198:117–333

    Google Scholar 

  • Abualigah L, Shehab M, Alshinwan M, Alabool H (2019) Salp swarm algorithm: a comprehensive survey. Neural Comput Appl 32:11195–1215

    Google Scholar 

  • Abusnaina AA, Ahmad S, Jarrar R, Mafarja M (2018) Training neural networks using salp swarm algorithm for pattern classification. In: Proceedings of the 2nd International Conference on Future Networks and Distributed Systems, ACM, 17

  • Acan A, Ünveren A (2020) Multiobjective great deluge algorithm with two-stage archive support. Eng Appl Artif Intell 87:103239

    Google Scholar 

  • Ahmad MW, Mourshed M, Yuce B, Rezgui Y (2016) Computational intelligence techniques for hvac systems: a review. Building simulation, vol 9. Springer, New York, pp 359–398

    Google Scholar 

  • Ahmadian A, Elkamel A, Mazouz A (2019) An improved hybrid particle swarm optimization and tabu search algorithm for expansion planning of large dimension electric distribution network. Energies 12(16):3052

    Google Scholar 

  • Albashish D, Hammouri AI, Braik M, Atwan J, Sahran S (2021) Binary biogeography-based optimization based svm-rfe for feature selection. Appl Soft Comput 101:107026

    Google Scholar 

  • Al-Betar MA (2017) beta-hill climbing: an exploratory local search. Neural Comput Appl 28(1):153–168

    Google Scholar 

  • Aldabbas H, Albashish D, Khatatneh Amin R (2022) An architecture of iot-aware healthcare smart system by leveraging machine learning. Int Arab J Inf Technol 19(2):160–172

    Google Scholar 

  • Al-Laham M, Abdullah S, Al-Ma’aitah MA, Al-Betar MA, Kassaymeh S, Azzazi A (2023) Parameter identification of a multilayer perceptron neural network using an optimized salp swarm algorithm. Int J Adv Comput Sci Appl 4(6):1224

    Google Scholar 

  • Jaddi NS, Abdullah S (2013) Hybrid of genetic algorithm and great deluge algorithm for rough set attribute reduction. Turk J Electr Eng Comput Sci 21(6):1737–1750

  • Alweshah M (2019) Construction biogeography-based optimization algorithm for solving classification problems. Neural Comput Appl 31(10):5679–5688

    Google Scholar 

  • Alweshah M, Al-Daradkeh A, Al-Betar MA, Almomani A, Oqeili S (2019) beta-hill climbing algorithm with probabilistic neural network for classification problems. J Ambient Intell Hum Comput 11:3405–3416

    Google Scholar 

  • Alweshah M, Qadoura MA, Hammouri AI, Azmi MS, AlKhalaileh S (2020) Flower pollination algorithm for solving classification problems. Int J Adv Soft Comput Appl 12(1):15–34

    Google Scholar 

  • Alweshah M, Al-Sendah M, Dorgham OM, Al-Momani A, Tedmori S (2020) Improved water cycle algorithm with probabilistic neural network to solve classification problems. Clust Comput 23:2703–2718

    Google Scholar 

  • Alweshah M, Rababa L, Ryalat MH, Al Momani A, Ababneh MF (2022) African buffalo algorithm: training the probabilistic neural network to solve classification problems. J King Saud Univ Comput Inf Sci 34(5):1808–1818

    Google Scholar 

  • Alweshah M, Kassaymeh S, Alkhalaileh S, Almseidin M, Altarawni I (2023) An efficient hybrid mine blast algorithm for tackling software fault prediction problem. Neural Process Lett. https://doi.org/10.1007/s11063-023-11357-3

    Article  Google Scholar 

  • Alweshah M, Ramadan E, Ryalat MH, Almi’ani M, Hammouri AI (2020) Water evaporation algorithm with probabilistic neural network for solving classification problems. Jord J Comput Inf Technol 6(01)

  • Arnuphaptrairong T (2013) Early stage software effort estimation using function point analysis: an empirical validation. Int J Des Anal Tools Integr Circ Syst 4(1):15

    Google Scholar 

  • Asaithambi S, Rajappa M (2018) Swarm intelligence-based approach for optimal design of cmos differential amplifier and comparator circuit using a hybrid salp swarm algorithm. Rev Sci Instrum 89(5):054702

    Google Scholar 

  • Azadeh A, Ghaderi S, Sohrabkhani S (2008) Annual electricity consumption forecasting by neural network in high energy consuming industrial sectors. Energy Convers Manag 49(8):2272–2278

    Google Scholar 

  • Azzeh M (2011) Software effort estimation based on optimized model tree. In: Proceedings of the 7th International Conference on Predictive Models in Software Engineering, pp 1–8

  • Azzeh M Dataset quality assessment: An extension for analogy based effort estimation, arXiv preprint arXiv:1703.04575

  • Azzeh M, Nassif AB (2016) A hybrid model for estimating software project effort from use case points. Appl Soft Comput 49:981–989

    Google Scholar 

  • Azzeh M, Nassif AB, Banitaan S (2017) Comparative analysis of soft computing techniques for predicting software effort based use case points. IET Softw 12(1):19–29

    Google Scholar 

  • Basgalupp MP, Barros RC, Ruiz DD (2012) Predicting software maintenance effort through evolutionary-based decision trees. In: Proceedings of the 27th Annual ACM Symposium on Applied Computing, pp 1209–1214

  • Benala TR, Bandarupalli R (2016) Least square support vector machine in analogy-based software development effort estimatio. In: 2016 International Conference on Recent Advances and Innovations in Engineering (ICRAIE), IEEE, 1–6

  • Bocheng Z, Kuo L, Dinghao L, Jing L, Xuan F (2015) Short-term prediction of building energy consumption based on galm neural network. In: International Conference on Advances in Mechanical Engineering and Industrial Informatics (AMEII), pp 867–71

  • Braga PL, Oliveira AL, Meira SR (2008) A ga-based feature selection and parameters optimization for support vector regression applied to software effort estimation. In: Proceedings of the 2008 ACM Symposium on Applied Computing, pp 1788–1792

  • Britto R, Mendes E, Börstler J, (2015) An empirical investigation on effort estimation in agile global software development. In: IEEE 10th International Conference on Global Software Engineering. IEEE 2015:38–45

  • Chandrasekaran R, Kumar RV (2012) On the estimation of the software effort and schedule using constructive cost model ii and functional point analysis. Int J Comput Appl 44(9):38–44

    Google Scholar 

  • Cheng S, Quilodrán-Casas C, Ouala S, Farchi A, Liu C, Tandeo P, Fablet R, Lucor D, Iooss B, Brajard J et al (2023) Machine learning with data assimilation and uncertainty quantification for dynamical systems: a review, arXiv preprint arXiv:2303.10462

  • Chhabra S, Singh H (2020) Optimizing design of fuzzy model for software cost estimation using particle swarm optimization algorithm. Int J Comput Intell Appl 19(01):2050005

    Google Scholar 

  • Chhabra S, Singh H (2020) Optimizing design of fuzzy model for software cost estimation using particle swarm optimization algorithm. Int J Comput Intell Appl 19(01):2050005

    Google Scholar 

  • Chirra SMR, Reza H (2019) A survey on software cost estimation techniques. J Softw Eng Appl 12(6):226

    Google Scholar 

  • de Barcelos Tronto IF, da Silva JDS, Sant’Anna N (2007) Comparison of artificial neural network and regression models in software effort estimation. In: 2007 International Joint Conference on Neural Networks, pp 771–776. IEEE

  • De Carvalho HDP, Fagundes R, Santos W (2021) Extreme learning machine applied to software development effort estimation. IEEE Access 9:92676–92687

    Google Scholar 

  • Dosdoğru AT, Boru M, Göçken A, ÖZÇALICI M, Göçken T (2018) Assessment of hybrid artificial neural networks and metaheuristics for stock market forecasting. J Cukurova Univ Inst Soc Sci 27(1):63–78

  • Dueck G (1993) New optimization heuristics: the great deluge algorithm and the record-to-record travel. J Comput Phys 104(1):86–92

    MathSciNet  MATH  Google Scholar 

  • Ekici BB, Aksoy UT (2009) Prediction of building energy consumption by using artificial neural networks. Adv Eng Softw 40(5):356–362

    MATH  Google Scholar 

  • Ekinci S, Hekimoglu B (2018) Parameter optimization of power system stabilizer via salp swarm algorithm. In: 2018 5th International Conference on Electrical and Electronic Engineering (ICEEE), pp 143–147. IEEE

  • El-Fergany AA (2018) Extracting optimal parameters of pem fuel cells using salp swarm optimizer. Renew Energy 119:641–648

    Google Scholar 

  • Eng K, Muhammed A, Mohamed MA, Hasan S (2020) A hybrid heuristic of variable neighbourhood descent and great deluge algorithm for efficient task scheduling in grid computing. Eur J Oper Res 284(1):75–86

    MathSciNet  MATH  Google Scholar 

  • Fadhil AA, Alsarraj RG, Altaie AM (2020) Software cost estimation based on dolphin algorithm. IEEE Access 8:75279–75287

    Google Scholar 

  • Faris H, Mafarja MM, Heidari AA, Aljarah I, Ala’M A-Z, Mirjalili S, Fujita H (2018) An efficient binary salp swarm algorithm with crossover scheme for feature selection problems. Knowl-Based Syst 154:43–67

    Google Scholar 

  • Felipe NF, Cavalcanti RP, Maia EHB, Amaral WP, Farnese AC, Tavares LD, de Faria ESJ, da Silva CIP, de Pádua Paula Filho W etal A comparative study of three test effort estimation methods. Revista Cubana Ciencias Informáticas 8

  • Gabrani G, Saini N, (2016) Effort estimation models using evolutionary learning algorithms for software development. In: Symposium on Colossal Data Analysis and Networking (CDAN), pp 1–6. IEEE

  • Gharehchopogh FS, Abdollahzadeh B, Arasteh B (2022) An improved farmland fertility algorithm with hyper-heuristic approach for solving travelling salesman problem. CMES-COMPUTER MODELING IN ENGINEERING and SCIENCES

  • Gharehchopogh FS (2022) An improved harris hawks optimization algorithm with multi-strategy for community detection in social network. J Bionic Eng 20(3):1175–1197

    Google Scholar 

  • Gharehchopogh FS (2023) Quantum-inspired metaheuristic algorithms: comprehensive survey and classification. Artif Intell Rev 56(6):5479–5543

    Google Scholar 

  • Gharehchopogh FS, Maleki I, Dizaji ZA (2022) Chaotic vortex search algorithm: metaheuristic algorithm for feature selection. Evol Intel 15(3):1777–1808

    Google Scholar 

  • Gharehchopogh FS, Namazi M, Ebrahimi L, Abdollahzadeh B (2023) Advances in sparrow search algorithm: a comprehensive survey. Arch Comput Methods Eng 30(1):427–455

    Google Scholar 

  • Gharehchopogh FS, Ucan A, Ibrikci T, Arasteh B, Isik G (2023) Slime mould algorithm: a comprehensive survey of its variants and applications. Arch Comput Methods Eng 30(4):2683–2723

    Google Scholar 

  • Ghatasheh N, Faris H, I. Aljarah RM (2019) Al-Sayyed, Optimizing software effort estimation models using firefly algorithm. arXiv preprint 8(3):133–142

  • Glover F, Laguna M (1998) Tabu search. In: Pardalos PM, Du D-Z (eds) Handbook of combinatorial optimization. Springer, Boston, pp 2093–2229

    Google Scholar 

  • Göçken M, Özçalıcı M, Boru A, Dosdoğru AT (2019) Stock price prediction using hybrid soft computing models incorporating parameter tuning and input variable selection. Neural Comput Appl 31(2):577–592

    Google Scholar 

  • Guha R, Ghosh M, Kapri S, Shaw S, Mutsuddi S, Bhateja V, Sarkar R (2019) Deluge based genetic algorithm for feature selection. Evol Intell 14:357–367

    Google Scholar 

  • Hecht-Nielsen R (1992) Theory of the backpropagation neural network. Neural networks for perception. Elsevier, Amsterdam, pp 65–93

    Google Scholar 

  • Heemstra FJ (1992) Software cost estimation. Inf Softw Technol 34(10):627–639

    Google Scholar 

  • Hornik K, Stinchcombe M, White H et al (1989) Multilayer feedforward networks are universal approximators. Neural Netw 6:359–366

    MATH  Google Scholar 

  • Hussien AG, Hassanien AE, Houssein EH (2017) Swarming behaviour of salps algorithm for predicting chemical compound activities. In: 2017 Eighth International Conference on Intelligent Computing and Information Systems (ICICIS), pp 315–320. IEEE

  • Ibrahim RA, Ewees AA, Oliva D, Elaziz MA, Lu S (2018) Improved salp swarm algorithm based on particle swarm optimization for feature selection. J Ambient Intell Hum Comput 10:3155–3169

    Google Scholar 

  • Ibrahim A, Ahmed A, Hussein S, Hassanien AE (2018) Fish image segmentation using salp swarm algorithm. In: International Conference on Advanced Machine Learning Technologies and Applications, pp 42–51. Springer

  • Jorgensen M, Shepperd M (2006) A systematic review of software development cost estimation studies. IEEE Trans Software Eng 33(1):33–53

    Google Scholar 

  • Kamal MW, Ahmed MA (2011) A proposed framework for use case based effort estimation using fuzzy logic: building upon the outcomes of a systematic literature review. Int J New Comput Architect Appl 1(4):953–976

    Google Scholar 

  • Kassaymeh S, Abdullah S, Alweshah M, Hammouri AI (2021a) A hybrid salp swarm algorithm with artificial neural network model for predicting the team size required for software testing phase. In: 2021 International Conference on Electrical Engineering and Informatics (ICEEI), pp 1–6. IEEE

  • Kassaymeh S, Abdullah S, Al-Laham M, Alah M, Al-Betar MA, Othman Z (2021b) Salp swarm optimizer for modeling software reliability prediction problems. Neural Process Lett 53:4451–4487

    Google Scholar 

  • Kassaymeh S, Abdullah S, Al-Betar MA, Alweshah M (2022a) Salp swarm optimizer for modeling the software fault prediction problem. J King Saud Univ Comput Inf Sci 34(6):3365–3378

    Google Scholar 

  • Kassaymeh S, Abdullah S, Al-Betar M, Alweshah M, Al-Laham M, Othman Z (2022b) Self-adaptive salp swarm algorithm for optimization problems. Soft Comput https://doi.org/10.21203/rs.3.rs-1600365/v1

  • Kassaymeh S, Al-Laham M, Al-Betar MA, Alweshah M, Abdullah S, Makhadmeh SN (2022c) Backpropagation neural network optimization and software defect estimation modelling using a hybrid salp swarm optimizer-based simulated annealing algorithm. Knowl-Based Syst 244:108511

    Google Scholar 

  • Kassaymeh S, Alweshah M, Al-Betar MA, Hammouri AI, Al-Ma’aitah MA (2023) Software effort estimation modeling and fully connected artificial neural network optimization using soft computing techniques. Clust Comput. https://doi.org/10.1007/s10586-023-03979-y

    Article  Google Scholar 

  • Kaushik A, Choudhary N, et al (2021) Software cost estimation using lstm-rnn. In: Proceedings of International Conference on Artificial Intelligence and Applications, pp 15–24. Springer

  • Khamees M, Albakry A, Shaker K (2018) Multi-objective feature selection: Hybrid of salp swarm and simulated annealing approach. In: International Conference on New Trends in Information and Communications Technology Applications, pp , 129–142. Springer

  • Kumar L, Rath SK (2016) Hybrid functional link artificial neural network approach for predicting maintainability of object-oriented software. J Syst Softw 121:170–190

    Google Scholar 

  • Kumar L, Rath SK (2017) Software maintainability prediction using hybrid neural network and fuzzy logic approach with parallel computing concept. Int J Syst Assur Eng Manag 8(2):1487–1502

    Google Scholar 

  • Kumar R, Aggarwal R, Sharma J (2013) Energy analysis of a building using artificial neural network: a review. Energy Build 65:352–358

    Google Scholar 

  • Kumar A, Gupta A, Jain A, Farma V (2020) Comparative study on swarm based algorithms for feature reduction in twitter sentiment analysis on figurative language. In: Future of Information and Communication Conference, pp 1–16. Springer

  • Li X, Jiang Q, Hsu MK, Chen Q (2019) Support or risk? Software project risk assessment model based on rough set theory and backpropagation neural network. Sustainability 11(17):4513

    Google Scholar 

  • Lopez-Martin C, Yáñez-Márquez C, Gutierrez-Tornes A (2006) A fuzzy logic model for software development effort estimation at personal level. In: Mexican International Conference on Artificial Intelligence, pp 122–133. Springer

  • Ma B, Ni H, Zhu X, Zhao R (2019) A comprehensive improved salp swarm algorithm on redundant container deployment problem. IEEE Access 7:136452–136470

    Google Scholar 

  • Mahmoud K, Abdel-Nasser M, Mustafa E, Ali ZM (2020) Improved salp-swarm optimizer and accurate forecasting model for dynamic economic dispatch in sustainable power systems. Sustainability 12(2):576

    Google Scholar 

  • Makhadmeh SN, Al-Betar MA, Assaleh K, Kassaymeh S (2022) A hybrid white shark equilibrium optimizer for power scheduling problem based iot. IEEE Access 10:132212–132231

    Google Scholar 

  • Makhadmeh SN, Al-Betar MA, Doush IA, Awadallah MA, Kassaymeh S, Mirjalili S, Zitar RA (2023) Recent advances in grey wolf optimizer, its versions and applications. IEEE Access. https://doi.org/10.1109/ACCESS.2023.3304889

    Article  Google Scholar 

  • Malhotra R, Jain A (2011) Software effort prediction using statistical and machine learning methods. Int J Adv Comput Sci Appl 2(1):145–152

    Google Scholar 

  • Malhotra R, Khanna M (2018) Particle swarm optimization-based ensemble learning for software change prediction. Inf Softw Technol 102:65–84

    Google Scholar 

  • Mensah S, Keung J, Bosu MF, Bennin KE (2018) Duplex output software effort estimation model with self-guided interpretation. Inf Softw Technol 94:1–13

    Google Scholar 

  • Minku LL, Yao X (2013) Ensembles and locality: insight on improving software effort estimation. Inf Softw Technol 55(8):1512–1528

    Google Scholar 

  • Mirjalili S, Gandomi AH, Mirjalili SZ, Saremi S, Faris H, Mirjalili SM (2017) Salp swarm algorithm: a bio-inspired optimizer for engineering design problems. Adv Eng Softw 114:163–191

    Google Scholar 

  • Mohammadzadeh H, Gharehchopogh FS (2021) A multi-agent system based for solving high-dimensional optimization problems: a case study on email spam detection. Int J Commun Syst 34(3):e4670

    Google Scholar 

  • Mohri M, Rostamizadeh A, Talwalkar A (2018) Foundations of machine learning. MIT Press, Cambridge

    MATH  Google Scholar 

  • Muklason A, Syahrani GB, Marom A (2019) Great deluge based hyper-heuristics for solving real-world university examination timetabling problem: new data set and approach. Procedia Comput Sci 161:647–655

    Google Scholar 

  • Nagar C, Dixit A (2012) Efforts estimation by combining the use case point and cocomo. Int J Comput Appl 52(7)

  • Naseri TS, Gharehchopogh FS (2022) A feature selection based on the farmland fertility algorithm for improved intrusion detection systems. J Netw Syst Manag 30(3):40

    Google Scholar 

  • Nassif AB, Azzeh M, Idri A, Abran A (2019) Software development effort estimation using regression fuzzy models. Comput Intell Neurosci

  • Ochodek M (2016) Functional size approximation based on use-case names. Inf Softw Technol 80:73–88

    Google Scholar 

  • Okoh C, Roy R, Mehnen J, Redding L (2014) Overview of remaining useful life prediction techniques in through-life engineering services. Procedia Cirp 16:158–163

    Google Scholar 

  • Oliveira AL, Braga PL, Lima RM, Cornélio ML (2010) Ga-based method for feature selection and parameters optimization for machine learning regression applied to software effort estimation. Inf Softw Technol 52(11):1155–1166

    Google Scholar 

  • Paterson J, Ombuki-Berman B (2018) Optimizing scale-free network robustness with the great deluge algorithm. In: International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, pp 434–446. Springer

  • Patil LV, Shivale NM, Joshi S, Khanna V (2014) Improving the accuracy of cbsd effort estimation using fuzzy logic. In: IEEE International Advance Computing Conference (IACC). IEEE 2014, pp 1385–1391

  • Qasem A, Sheikh Abdullah SNH, Sahran S, Albashish D, Goudarzi S, Arasaratnam S (2022) An improved ensemble pruning for mammogram classification using modified bees algorithm. Neural Comput Appl 34(12):10093–10116

    Google Scholar 

  • Rahman MT, Islam MM, (2019) A comparison of machine learning algorithms to estimate effort in varying sized software. In: IEEE Region 10 Symposium (TENSYMP), pp 137–142. IEEE

  • Rajkumar M, Asokan P, Anilkumar N, Page T (2011) A grasp algorithm for flexible job-shop scheduling problem with limited resource constraints. Int J Prod Res 49(8):2409–2423

    Google Scholar 

  • Reddy DKK, Behera H (2020) Software effort estimation using particle swarm optimization: Advances and challenges. In: Computational Intelligence in Pattern Recognition, pp 243–258. Springer

  • Resmi V, Vijayalakshmi S, Chandrabose RS (2019) An effective software project effort estimation system using optimal firefly algorithm. Clust Comput 22(5):11329–11338

    Google Scholar 

  • Rumelhart DE, Durbin R, Golden R, Chauvin Y (1995) Backpropagation: The basic theory, Backpropagation: Theory, architectures and applications 1–34

  • Sabrjoo S, Khalili M, Nazari M (2015) Comparison of the accuracy of effort estimation methods. In: 2015 2nd International Conference on Knowledge-Based Engineering and Innovation (KBEI), pp 724–728. IEEE

  • Sayed GI, Khoriba G, Haggag MH (2018) A novel chaotic salp swarm algorithm for global optimization and feature selection. Appl Intell 48(10):3462–3481

    Google Scholar 

  • Schneider M, Stenger A, Hof J (2015) An adaptive vns algorithm for vehicle routing problems with intermediate stops. OR Spectr 37(2):353–387

    MATH  Google Scholar 

  • Schwalbe K (2015) Information technology project management. In: Cengage learning

  • Sehra SK, Brar YS, Kaur N, Sehra SS (2019) Software effort estimation using fahp and weighted kernel lssvm machine. Soft Comput 23(21):10881–10900

    Google Scholar 

  • Shah MA, Jawawi DNA, Isa MA, Younas M, Abdelmaboud A, Sholichin F (2020) Ensembling artificial bee colony with analogy-based estimation to improve software development effort prediction. IEEE Access 8:58402–58415

    Google Scholar 

  • Sheta AF, Abdel-Raouf A (2016) Estimating the parameters of software reliability growth models using the grey wolf optimization algorithm. IJACSA 7(4)

  • Sheta AF (2006) Estimation of the cocomo model parameters using genetic algorithms for nasa software projects. J Comput Sci 2(2):118–123

    Google Scholar 

  • Sheta AF, Kassaymeh S, Rine D (2014) Estimating the number of test workers necessary for a software testing process using artificial neural networks. IJACSA 5(7):186–192

    Google Scholar 

  • Sheta AF, Rine D, Kassaymeh S (2015) Software effort and function points estimation models based radial basis function and feedforward artificial neural networks. Int J Next-Gener Comput 6(3):192–205

    Google Scholar 

  • Sheta A, Aljahdali S, Braik M (2018) Utilizing faults and time to finish estimating the number of software test workers using artificial neural networks and genetic programming. In: International Conference Europe Middle East & North Africa Information Systems and Technologies to Support Learning, Springer, pp 613–624

  • Shishavan ST, Gharehchopogh FS (2022) An improved cuckoo search optimization algorithm with genetic algorithm for community detection in complex networks. Multimed Tools Appl 81(18):25205–25231

    Google Scholar 

  • Silhavy R, Silhavy P, Prokopova Z (2017) Analysis and selection of a regression model for the use case points method using a stepwise approach. J Syst Softw 125:1–14

    Google Scholar 

  • Silhavy R, Silhavy P, Prokopova Z (2018) Evaluating subset selection methods for use case points estimation. Inf Softw Technol 97:1–9

    Google Scholar 

  • Song Q, Shepperd M (2011) Predicting software project effort: a grey relational analysis based method. Expert Syst Appl 38(6):7302–7316

    Google Scholar 

  • Sree SR, Rao CP (2020) A study on application of soft computing techniques for software effort estimation. In: A journey towards bio-inspired techniques in software engineering, pp 141–165. Springer, New York

  • Tohma Y, Tokunaga K, Nagase S, Murata Y (1989) Structural approach to the estimation of the number of residual software faults based on the hyper-geometric distribution. IEEE Trans Software Eng 15(3):345–355

    Google Scholar 

  • D. Toka, O. Turetken, (2013) Accuracy of contemporary parametric software estimation models: a comparative analysis. In: 2013 39th Euromicro Conference on Software Engineering and Advanced Applications, pp 313–316. IEEE

  • Tubishat M, Idris N, Shuib L, Abushariah MA, Mirjalili S (2020) Improved salp swarm algorithm based on opposition based learning and novel local search algorithm for feature selection. Expert Syst Appl 145:113–122

    Google Scholar 

  • Van Laarhoven PJ, Aarts EH (1987) Simulated annealing. Simulated annealing: theory and applications. Springer, New York, pp 7–15

    MATH  Google Scholar 

  • Wang J, Gao Y, Chen X (2018) A novel hybrid interval prediction approach based on modified lower upper bound estimation in combination with multi-objective salp swarm algorithm for short-term load forecasting. Energies 11(6):1561

    Google Scholar 

  • Wang Y, Wang L, Chang Q, Yang C (2019) Effects of direct input-output connections on multilayer perceptron neural networks for time series prediction. Soft Comput 24:4729–38

    Google Scholar 

  • Wang S, Jia H, Peng X (2020) Modified salp swarm algorithm based multilevel thresholding for color image segmentation. Math Biosci Eng 17:700–724

    MathSciNet  MATH  Google Scholar 

  • Wei Y, Wan W, You X, Cheng F, Wang Y (2023) Improved salp swarm algorithm for tool wear prediction. Electronics 12(3):769

    Google Scholar 

  • Wen J, Li S, Lin Z, Hu Y, Huang C (2012) Systematic literature review of machine learning based software development effort estimation models. Inf Softw Technol 54(1):41–59

    Google Scholar 

  • Wickramaarachchi D, Lai R (2017) Effort estimation in global software development—a systematic review. Comput Sci Inf Syst 14(2):393–421

    Google Scholar 

  • Wittek P (2014) Pattern recognition and neural networks. Quantum machine learning. Elsevier, New York, pp 63–71

    MATH  Google Scholar 

  • Wu J, Nan R, Chen L (2019) Improved salp swarm algorithm based on weight factor and adaptive mutation. J Exp Theor Artif Intell 31(3):493–515

    Google Scholar 

  • Xing Z, Jia H (2019) Multilevel color image segmentation based on glcm and improved salp swarm algorithm. IEEE Access 7:37672–37690

    Google Scholar 

  • Xiongzi C, Jinsong Y, Diyin T, Yingxun W (2011) Remaining useful life prognostic estimation for aircraft subsystems or components: A review. In: IEEE 2011 10th International Conference on Electronic Measurement & Instruments, 2, IEEE, pp 94–98

  • Yousef QM, Alshaer YA, Alhammad NK (2017) Dragonfly estimator: a hybrid software projects’ efforts estimation model using artificial neural network and dragonfly algorithm. Int J Comput Sci Netw Secur 17(9):108–120

    Google Scholar 

  • Zaman HRR, Gharehchopogh FS (2022) An improved particle swarm optimization with backtracking search optimization algorithm for solving continuous optimization problems. Eng Comput 38(Suppl 4):2797–2831

    Google Scholar 

  • Zhang J, Wang J (2020) Improved salp swarm algorithm based on levy flight and sine cosine operator. IEEE Access 8:99740–99771

    Google Scholar 

  • Zhang W, Huang L, Ng V, Ge J (2015) Smplearner: learning to predict software maintainability. Autom Softw Eng 22(1):111–141

    Google Scholar 

  • Zhang H, Cai Z, Ye X, Wang M, Kuang F, Chen H, Li C, Li Y (2020) A multi-strategy enhanced salp swarm algorithm for global optimization. Eng Comput 38:1177–1203

    Google Scholar 

  • Zhang L, Li C, Wu Y, Huang J, Cui Z (2020) An improved salp swarm algorithm with spiral flight search for optimizing hybrid active power filters’ parameters. IEEE Access 8:154816–154832

    Google Scholar 

  • Zitar RA, Al-Betar M, Ryalat M, Kassaymehd S (2023) A review of uav visual detection and tracking methods. arXiv preprint arXiv arXiv–2306

Download references

Acknowledgements

This work was supported by the Universiti Kebangsaan Malaysia under Grant Number: FRGS/1/2019/ICT02/UKM/01/1.

Author information

Authors and Affiliations

  1. Software Engineering Department, Faculty of Information Technology, Aqaba University of Technology, Aqaba, Jordan

    Sofian Kassaymeh

  2. Data Mining and Optimization Research Group, Center for Artificial Intelligence Technology, Universiti Kebangsaan Malaysia, Bangi Selangor, Malaysia

    Sofian Kassaymeh & Salwani Abdullah

  3. Artificial Intelligence Research Center, College of Engineering and Information Technology, Ajman University, Ajman, United Arab Emirates

    Sofian Kassaymeh, Mohammed Azmi Al-Betar & Sharif Naser Makhadmeh

  4. Department of Information Technology, Al-Huson University College, Al-Balqa Applied University, Irbid, Jordan

    Mohammed Azmi Al-Betar

  5. Department of Computer Science, Prince Abdullah bin Ghazi Faculty of CIT, Al-Balqa Applied University, Salt, Jordan

    Mohammed Alweshah

  6. Artificial Intelligence Department, Faculty of Information Technology, Aqaba University of Technology, Aqaba, Jordan

    Mohammed Alweshah

  7. Department of Computer Science, Faculty of Information Technology, Zarqa University, Zarqa, Jordan

    Amer Abu Salem

  8. Department of Data Science and Artificial Intelligence, University of Petra, Amman, Jordan

    Sharif Naser Makhadmeh

  9. MIS Department, Amman University College, Amman, Jordan

    Mohammad Atwah Al-Ma’aitah

Authors
  1. Sofian Kassaymeh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Salwani Abdullah
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mohammed Azmi Al-Betar
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mohammed Alweshah
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Amer Abu Salem
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Sharif Naser Makhadmeh
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Mohammad Atwah Al-Ma’aitah
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sofian Kassaymeh.

Ethics declarations

Conflict of interest

The authors declare that there are no conflicts of interest regarding the publication of this paper.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kassaymeh, S., Abdullah, S., Al-Betar, M.A. et al. An enhanced salp swarm optimizer boosted by local search algorithm for modelling prediction problems in software engineering. Artif Intell Rev 56 (Suppl 3), 3877–3925 (2023). https://doi.org/10.1007/s10462-023-10618-w

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10462-023-10618-w

Keywords

Search

Navigation