SFFHO: Development of Statistical Fitness-based Fire Hawk Optimizer for Software Testing and Maintenance Approach using Adaptive Deep Learning Method (original) (raw)

Abstract

Software maintenance is the trendsetting activity of modifying, fixing and enhancing the software code deliverables once it is turned over to the client. Evolving with large-scale data available in the system, the software system indulges in deriving the software maintenance. Though software testing and maintenance are critical, it is costly and time-consuming operations in the software development lifecycle. Moreover, the conventional approaches struggle to keep pace with the quick evolution of software systems, resulting in inaccurate or incomplete testing, security vulnerabilities, and undetected bugs. The conventional approaches such as existing testing models, code reviews, and manual testing, often struggle to keep pace with the quick updates and changes to software models, resulting in an important increase in maintenance and testing costs, effort, and time. In addition, the traditional models mostly depend on simplistic assumptions about software behaviour, ignoring the complex relationships among the environmental factors, user interactions, and software components. This oversimplification can result in inaccurate or incomplete testing, leading to unidentified bugs, security concerns, and performance problems that can have serious problems in critical software systems. Therefore, there is an urgent requirement for innovative models that can efficiently maintain and test the software systems, guaranteeing their performance, security, and reliability. This work develops a novel Adaptive Ensemble Deep Learning (AEDL) and Statistical Fitness-based Fire Hawk Optimizer (SFFHO) for revolutionizing software testing and maintenance. This work presents a new software testing and maintenance approach utilizing a deep learning approach. At first, the significant data is fetched from available data resources. Further, the collected data is subjected to the Adaptive Ensemble Deep Learning (AEDL) model. Here, the AEDL model is built with the combination of the Variational Autoencoder (VAE), Attention-based Convolutional Neural Network (ACNN) and Dilated Recurrent Neural Network (DRNN). The developed model helps maintain and test the software based on the client's requirements. Moreover, to enhance the system, the hyper-parameter of AEDL is tuned by the Statistical Fitness-based Fire Hawk Optimizer (SFFHO). Finally, the designed system is contrasted with other traditional approaches that prove better efficiency.

Access this article

Log in via an institution

Subscribe and save

Buy Now

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

Instant access to the full article PDF.

Similar content being viewed by others

Data Availability

Change history

The original online version of this article was revised due to incorrect order of affiliations.

References

  1. Zhang N, Ying S, Zhu K, Zhu D (2021) Software defect prediction based on stacked sparse denoising autoencoders and enhanced extreme learning machine. 16(1):29-47
  2. Cerón-Figueroa S, López-Martín C (2020) Stochastic gradient boosting for predicting the maintenance effort of software-intensive systems. 14(2):82-87
  3. Sharma T, Jatain A, Bhaskar S, Pabreja K (2023) Ensemble machine learning paradigms in software defect prediction. Procedia Comput Sci 218:199–209
    Article Google Scholar
  4. Braiek HB, Khomh F (2020) On testing machine learning programs. J Syst Softw 164:110542
    Article Google Scholar
  5. Lehmann C, Huber LG, Horisberger T, Scheiba G, Sima AC, Stockinger K (2020) Big Data architecture for intelligent maintenance: a focus on query processing and machine learning algorithms. 7
  6. Rogowski R, Werbinska-Wojciechowska S (2024) Proactive Maintenance of Pump Systems in Mining Shaft Dewatering: Integrating Sensor Instrumentation and Advanced Measurement Techniques, Sensors and Electronic Instrumentation Advances 191
  7. Chen Y-T, Huang C-Y, Yang T-H (2023) Using multi-pattern clustering methods to improve software maintenance quality. 17(1):22
  8. Segun-Falade OD, Osundare OS, Kedi WE, Okeleke PA, Ijomah TI, Abdul-Azeez OY (2024) Assessing the transformative impact of cloud computing on software deployment and management. Comput Sci IT Res J 5(8):2062
    Article Google Scholar
  9. Zhu X, Li N, Wang Y (February 2022) Software change-proneness prediction based on deep learning. 34(4):17
  10. Ikram A, AbdulJalil M, BinNgah A, Raza S, SalmanKhan A (June 2022) Project assessmentin offshore software maintenance outsourcing using deep extreme learning machines, computers, materials & Continua, 13
  11. Choetkiertikul M, Dam HK, Tran T, Pham T, Ragkhitwetsagul C, Ghose A (2021) Automatically recommending components for issue reports using deep learning. Empirical Softw Eng 26(14)
  12. Monsefi AK, Zakeri B, Samsam S, Khashehchi M (2019) Performing Software Test Oracle Based on Deep Neural Network with Fuzzy Inference System. High-Performance Comput Big Data Anal 891:406–417
    Google Scholar
  13. Alenezi M, Akour M, Al Qasem O (2020) Harnessing deep learning algorithms to predict software refactoring. 18(6)
  14. Malhotra R, Chug A (2012) Software Maintainability Prediction using Machine Learning Algorithms, an international J 2(2)
  15. SofianK Alweshah M, Al-Betar MA, Hammouri AI, Al-Maaitah MA (2024) Software effort estimation modeling and fully connected artificial neural network optimization using soft computing techniques. Cluster Comput 27(1):737–760
    Article Google Scholar
  16. Johnson D, McDonald JT, Benton RG (2024) and David Bourrie. Effectiveness of Image-Based Deep Learning on Token-Level Software Vulnerability Detection, IEEE Access, pp 1054–1063
    Google Scholar
  17. Jingda Y, Arya S, Wang Y (2024) Formal-guided fuzz testing: Targeting security assurance from specification to implementation for 5g and beyond. IEEE Access
  18. Siddiqui S, Khan TA (2019) Test patterns for cloud applications. IEEE Access 7:147060–147080
    Article Google Scholar
  19. Dinella E, Mytkowicz T, Svyatkovskiy A, Bird C, Naik M, Lahiri S (2023) DeepMerge: learning to merge programs. IEEE Trans Softw Eng 49(4):1599–1614
    Article Google Scholar
  20. Li K, Zhu A, Zhao P, Song J, Liu J (2024) Utilizing deep learning to optimize software development processes. J CompuT Technol Appl Math
  21. Borandag,E (2023) Software fault prediction using an RNN-based deep learning approach and ensemble machine learning techniques. Appl Sci 13(1639)
  22. Del Rey S, Martínez-Fernández S, Salmerón A (June 2023) Bayesian Network analysis of software logs for data-driven software maintenance. IET Software 17
  23. Sadman S Md, Islam Maruf Md S, Ahmed S, Dewan Md L, Islam Md R (2024) Discussion on the prospects of artificial intelligence in software management and maintenance. Eur J Theoretical Appl Sci 2(5)5:816-825
  24. Xiao L, Miao H, Shi T, Hong Yu (2020) LSTM-based deep learning for spatial–temporal software testing. Distribut Parallel Databases 38:687–712
    Article Google Scholar
  25. Bani-Salameh H, Sallam M, Al shboul B (2021) A deep-learning-based bug priority prediction using RNN-LSTM neural networks. ∗Department of Software Engineering 15:29–45
  26. Raffaella MM, Bianchini E, Sinceri S, Francesconi M, Gemignani V (2024) Software medical device maintenance: DevOps based approach for problem and modification management. J Softw: Evol Process 36(4)
  27. Gunduz MZ, Resul Das (2024) Smart Grid Security: An Effective Hybrid CNN-Based approach for detecting energy theft using consumption patterns. Sensors 24(4):1148
  28. Azizi M, Talatahari S, Gandomi AH (2022) Fire hawk optimizer: a novel metaheuristic algorithm. Artif Intell Rev
  29. Ji X, Zhang Y, Gong D, Sun X, Guo Y (2021) Multisurrogate-assisted multitasking particle swarm optimization for expensive multimodal problems. IEEE Trans Cybernetics 53:2516–2530
    Article Google Scholar
  30. Alsghaier H, Akour M (2020) Software fault prediction using particle swarm algorithm with genetic algorithm and support vector machine classifier. Softw: Pract Exp 50:407–427
    Google Scholar
  31. Kassaymeh S, Abdullah S, Al-Betar MA, Alweshah M (2022) Salp swarm optimizer for modeling the software fault prediction problem. J King Saud Univ-Comput Inform Sci 34:3365–3378
    Article Google Scholar
  32. Yang L, Li Z, Wang D, Miao H, Wang Z (2021) Software defects prediction based on hybrid particle swarm optimization and sparrow search algorithm. IEEE Access 9:60865–60879
    Article Google Scholar
  33. Das H, Das S, Gourisaria MK, Khan SB, Almusharraf A, Alharbi AI, TR M (2024) Enhancing software fault prediction through feature selection with spider wasp optimization algorithm. IEEE Access
  34. Eivazpour Z, Keyvanpour MR (2019) Improving Performance in Software Defect Prediction Using Variational Autoencoder, 2019 5th Conference on Knowledge Based Engineering and Innovation (KBEI), Tehran, Iran, pp 644–649
  35. Lu PC, Xu MB, Gao H (2019) An improved CNN model for within-project software defect prediction. Appl Sci 9(10)
  36. Zhang B, Xiao W, Xiao X, Kumar Sangaiah A, Zhang W, Zhang J (2020) Ransomware classification using patch-based CNN and self-attention network on embedded N-grams of opcodes. Future Gen Comput Syst 110:708–720
    Article Google Scholar
  37. Chang S, Zhang Y, Han W, Yu M, Guo X, Tan W, Cui X, Witbrock M, Hasegawa-Johnson MA, Huang TS (2017) Dilated recurrent neural networks. Adv Neural Inf Process Syst
  38. Oyelade ON, Ezugwu AE-S, Mohamed TIA, Abualigah L (2022) Ebola optimization search algorithm: a new nature-inspired metaheuristic optimization algorithm. IEEE Access 10:16150–16177
    Article Google Scholar
  39. Zhong C, Li G, Meng Z (2022) Beluga whale optimization: a novel nature-inspired metaheuristic algorithm. Knowledge-Based Syst 251
  40. Azizi M, Baghalzadeh Shishehgarkhaneh M, Basiri M, Moehler RC (2023) Squid Game Optimizer (SGO): a novel metaheuristic algorithm. Sci Rep 13(5373)
  41. Chongyan Z, Zhu J, Huang Z, Xie D, Huang Y (2023) "CAE-based case of improving material utilization of body sheet metal parts. Int Seminar Comput Sci Eng Technol (SCSET) 453–456
  42. Pourdaryaei A, Mohammadi M, Mubarak H, Abdellatif A, Karimi M, Gryazina E, Terzija V (2024) A new framework for electricity price forecasting via multi-head self-attention and CNN-based techniques in the competitive electricity market. Expert Syst Appl 235:121207
    Article Google Scholar
  43. Borandag E (2023) Software fault prediction using an RNN-based deep learning approach and ensemble machine learning techniques. Appl Sci 13(3):1639
    Article Google Scholar
  44. Abbas S, Aftab S, Khan MA, Ghazal TM, Hamadi HA (2023) Data and ensemble machine learning fusion based intelligent software defect prediction system. Comput Mater Continuam 75
  45. Abdu A, Zhai Z, Abdo HA, Algabri R (2024) Software defect prediction based on deep representation learning of source code from contextual syntax and semantic graph. IEEE Trans Reliab 73(2):820–834
    Article Google Scholar
  46. Fei Q, Hu H, Yin G, Sun Z (2025) "A software defect prediction method using a multivariate heterogeneous hybrid deep learning algorithm. Comput Mater Continua 82

Download references

Funding

This research did not receive any specific funding.

Author information

Authors and Affiliations

  1. Department of Information Technology, SSM Institute of Engineering and Technology, Dindigul, 624002, Tamil Nadu, India
    G. Prabu
  2. Department of Computer Science and Engineering, SSM Institute of Engineering and Technology, Dindigul, 624002, Tamil Nadu, India
    C. Sujatha
  3. Department of Computer Science and Engineering, Shri Angalamman College of Engineering and Technology, Trichy, 621105, Tamil Nadu, India
    J. Erin Shine
  4. Department of Electrical and Electronics Engineering, SSM Institute of Engineering and Technology, Dindigul, 624002, Tamil Nadu, India
    T. Arulkumar

Authors

  1. G. Prabu
  2. C. Sujatha
  3. J. Erin Shine
  4. T. Arulkumar

Contributions

Dr. G. Prabu, Dr. C. Sujatha, Mrs.J.Erin Shine, Dr.T.Arulkumar designed the model, computational framework and carried out the implementation. Dr. G. Prabu performed the calculations and wrote the manuscript with all the inputs. Dr. G. Prabu, Dr. C. Sujatha, Mrs.J.Erin Shine, Dr.T.Arulkumar discussed the results and contributed to the final manuscript.

Corresponding author

Correspondence toG. Prabu.

Ethics declarations

Ethics Approval

Not Applicable.

Not Applicable.

Conflict of Interest

The authors declare no conflict of interest.

Additional information

Responsible Editor: Y. K. Malaiya.

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

Cite this article

Prabu, G., Sujatha, C., Shine, J.E. et al. SFFHO: Development of Statistical Fitness-based Fire Hawk Optimizer for Software Testing and Maintenance Approach using Adaptive Deep Learning Method.J Electron Test 41, 313–338 (2025). https://doi.org/10.1007/s10836-025-06177-3

Download citation

Keywords