227
Views
7
CrossRef citations to date
0
Altmetric
Computers and computing

Enhancing Software Reliability and Fault Detection Using Hybrid Brainstorm Optimization-Based LSTM Model

, &

References

  • S. Sulman, and B. Nisar, “A review of software fault detection and correction process, models and techniques,” J. Softw. Eng. Intell. Syst., Vol. 3, no. 1, pp. 37–44, 2018.
  • M. Altenbernd, and D. Göddeke, “Soft fault detection and correction for multigrid,” Int. J. High. Perform. Comput. Appl., Vol. 32, no. 6, pp. 897–912, 2018.
  • L. Raamesh, S. Jothi, and S. Radhika, “Test case minimization and prioritization for regression testing using SBLA-based adaboost convolutional neural network,” The Journal of Supercomputing, Vol. 42, pp. 918, 2022. doi:10.1007/s11227-022-04540-1.
  • A. Chandrasekar, V. R. Rajasekar, and V. Vasudevan, “Improved authentication and key agreement protocol using elliptic curve cryptography,” International Journal of Computer Science and Security, Vol. 3, no. 4, pp. 325–333, 2009.
  • M. Bohman, B. James, M. J. Wirthlin, H. Quinn, and J. Goeders, “Microcontroller compiler-assisted software fault tolerance,” IEEE Trans. Nucl. Sci., Vol. 66, no. 1, pp. 223–232, 2018.
  • A. K. Shrivastava, and R. Sharma, “Developing a hybrid software reliability growth model,” Int. J. Qual. Reliab. Manag., 2021.
  • Q. Li, and H. Pham, “Modeling software fault-detection and fault-correction processes by considering the dependencies between fault amounts,” Appl. Sci., Vol. 11, no. 15, pp. 6998, 2021.
  • X. Cheng, Y. Wang, W. Zhou, X. Wang, and J. Wang, “Software fault detection for sequencing constraint defects,” Int. J. Perform. Eng., Vol. 16, no. 11, 2020.
  • Q. Li, and H. Pham, “Modeling software fault-detection and fault-correction processes by considering the dependencies between fault amounts,” Appl. Sci., Vol. 11, no. 15, pp. 6998, 2021.
  • V. Sundararaj, S. Muthukumar, and R. S. Kumar, “An optimal cluster formation based energy efficient dynamic scheduling hybrid MAC protocol for heavy traffic load in wireless sensor networks,” Comput. Secur., Vol. 77, pp. 277–288, 2018.
  • B. A. Hassan, “CSCF: a chaotic sine cosine firefly algorithm for practical application problems,” Neural Comput. Appl., Vol. 33, pp. 1–20, 2020.
  • B. A. Hassan, T. A. Rashid, and S. Mirjalili, “Formal context reduction in deriving concept hierarchies from corpora using adaptive evolutionary clustering algorithm star,” Compl. Intell. Syst., 1–16, 2021.
  • V. Sundararaj, “An efficient threshold prediction scheme for wavelet based ECG signal noise reduction using variable step size firefly algorithm,” Int. J. Intell. Eng. Syst., Vol. 9, no. 3, pp. 117–126, 2016.
  • M. M. Gowthul Alam, and S. Baulkani, “Geometric structure information based multi-objective function to increase fuzzy clustering performance with artificial and real-life data,” Soft. Comput., Vol. 23, no. 4, pp. 1079–1098, 2019a.
  • V. Sundararaj, “Optimised denoising scheme via opposition-based self-adaptive learning PSO algorithm for wavelet-based ECG signal noise reduction,” Int. J. Biomed. Eng. Technol., Vol. 31, no. 4, pp. 325, 2019.
  • V. Sundararaj, et al., “CCGPA-MPPT: Cauchy preferential crossover-based global pollination algorithm for MPPT in photovoltaic system,” Prog. Photovolt. Res. Appl., Vol. 28, no. 11, pp. 1128–1145, 2020.
  • M. R. Rejeesh, “Interest point based face recognition using adaptive neuro fuzzy inference system,” Multimed. Tools. Appl., Vol. 78, no. 16, pp. 22691–22710, 2019.
  • B. A. Hassan, and T. A. Rashid, “Datasets on statistical analysis and performance evaluation of backtracking search optimisation algorithm compared with its counterpart algorithms,” Data Brief, Vol. 28, pp. 105046, 2020.
  • M. M. Gowthul Alam, and S. Baulkani, “Local and global characteristics-based kernel hybridization to increase optimal support vector machine performance for stock market prediction,” Knowl. Inf. Syst., Vol. 60, no. 2, pp. 971–1000, 2019b.
  • K. Gao, “Simulated software testing process and its optimization considering heterogeneous debuggers and release time,” IEEE Access, Vol. 9, pp. 38649–38659, 2021.
  • H. Xiao, M. Cao, and R. Peng, “Artificial neural network based software fault detection and correction prediction models considering testing effort,” Appl. Soft. Comput., Vol. 94, pp. 106491, 2020.
  • C. Choudhary, P. K. Kapur, S. K. Khatri, R. Muthukumar, and A. K. Shrivastava, “Effort based release time of software for detection and correction processes using MAUT,” Int. J. Syst. Assur. Eng. Manag., Vol. 11, no. 2, pp. 367–378, 2020.
  • H. Okamura, and T. Dohi. “A generalized bivariate modeling framework of fault detection and correction processes,” in 2017 IEEE 28th International Symposium on Software Reliability Engineering (ISSRE), 2017, pp. 35–45. IEEE.
  • M. Zhu, and H. Pham, “A two-phase software reliability modeling involving with software fault dependency and imperfect fault removal,” Comput. Lang. Syst. Struct., Vol. 53, pp. 27–42, 2018.
  • T. Pham, and H. Pham, “A generalized software reliability model with stochastic fault-detection rate,” Ann. Oper. Res., Vol. 277, no. 1, pp. 83–93, 2019.
  • M. Zhu, and H. Pham, “A multi-release software reliability modeling for open source software incorporating dependent fault detection process,” Ann. Oper. Res., Vol. 269, no. 1, pp. 773–790, 2018.
  • M. das Chagas Moura, E. Zio, I. D. Lins, and E. Droguett, “Failure and reliability prediction by support vector machines regression of time series data,” Reliab. Eng. Syst. Saf., Vol. 96, no. 11, pp. 1527–1534, 2011.
  • A. J. Fedorec, C. M. Robinson, K. Y. Wen, and C. P. Barnes, “Flopr: an open source software package for calibration and normalization of plate reader and flow cytometry data,” ACS Synth. Biol., Vol. 9, no. 9, pp. 2258–2266, 2020.
  • E. K. Zavadskas, and Z. Turskis, “A new logarithmic normalization method in games theory,” Informatica, Vol. 19, no. 2, pp. 303–314, 2008.
  • B. J. Wittmann, Y. Yue, and F. H. Arnold, “Informed training set design enables efficient machine learning-assisted directed protein evolution,” Cell Syst., 2021.
  • T. Xia, Y. Song, Y. Zheng, E. Pan, and L. Xi, “An ensemble framework based on convolutional bi-directional LSTM with multiple time windows for remaining useful life estimation,” Comput. Ind., Vol. 115, pp. 103182, 2020.
  • S. Hochreiter, and J. Schmidhuber, “Long short-term memory,” Neural Comput., Vol. 9, no. 8, pp. 1735–1780, 1997.
  • F. Sherratt, A. Plummer, and P. Iravani, “Understanding LSTM network behaviour of IMU-based locomotion mode recognition for applications in prostheses and wearables,” Sensors, Vol. 21, no. 4, pp. 1264, 2021.
  • P. Leahy, G. Kiely, and G. Corcoran, “Structural optimisation and input selection of an artificial neural network for river level prediction,” J. Hydrol., Vol. 355, no. 1-4, pp. 192–201, 2008.
  • A. Osborn. Applied imagination-principles and procedures of creative writing. Read Books Ltd, 2012.
  • G. Zhang, L. Gao, and Y. Shi, “An effective genetic algorithm for the flexible job-shop scheduling problem,” Expert Syst. Appl., Vol. 38, no. 4, pp. 3563–3573, 2011.
  • M. Alzaqebah, S. Jawarneh, M. Alwohaibi, M. K. Alsmadi, I. Almarashdeh, and R. M. A. Mohammad, “Hybrid Brain Storm Optimization algorithm and Late Acceptance Hill Climbing to solve the Flexible job-shop scheduling problem,” J. King Saud Univ. Comp. Inform. Sci., 2020.
  • M. M. Drugan, and E. G. Talbi, “Adaptive multi-operator MetaHeuristics for quadratic assignment problems,” in EVOLVE – a bridge between probability, set oriented numerics, and evolutionary computation V, Cham: Springer, 2014, pp. 149–163.
  • K. Burke, and Y. Bykov. “A late acceptance strategy in hill-climbing for exam timetabling problems,” PATAT 2008 Conference, Montreal, Canada, 2008.
  • M. Alzaqebah, S. Jawarneh, M. Alwohaibi, M. K. Alsmadi, I. Almarashdeh, and R. M. A. Mohammad, “Hybrid Brain Storm Optimization algorithm and Late Acceptance Hill Climbing to solve the Flexible Job-Shop Scheduling problem,” J. King Saud Univ. Comp. Inform. Sci., 2020.
  • M. Bazargani, J. H. Drake, and E. K. Burke, “Late acceptance hill climbing for constrained covering arrays,” in International Conference on the Applications of Evolutionary computation, Cham: Springer, 2018, pp. 778–793.
  • J. D. Musa, “Software reliability measurement,” J. Syst. Softw., Vol. 1, pp. 223–241, 1979.
  • P. K. Kapur, S. Kumar, and R. B. Garg. Contributions to hardware and software reliability. World Scientific, 1999.
  • P. K. Kapur, H. Pham, A. Gupta, and P. C. Jha. Software reliability assessment with OR applications. Springer, 2011.
  • P. K. Kapur, H. Pham, A. Gupta, and P. C. Jha. Software reliability assessment with OR applications. London: Springer, 2011.
  • P. K. Diederik, and J. Ba. “Adam: a method for stochastic optimization.” arXiv preprint arXiv:1412.6980, 2014.
  • T. Jayalakshmi, and A. Santhakumaran, “Statistical normalization and back propagation for classification,” Int. J. Comp. Theory Eng., Vol. 3, no. 1, pp. 1793–8201, 2011.
  • R. Peng, and Q. ZhAi, “Modeling of software fault detection and correction processes with fault dependency,” Eksploatacja i Niezawodność, Vol. 19, no. 3, 2017.
  • P. J. Boland, and N. N. Chuív, “Optimal times for software release when repair is imperfect,” Stat. Probab. Lett., Vol. 77, no. 12, pp. 1176–1184, 2007.
  • D. Tang, L. Tang, R. Dai, J. Chen, X. Li, and J. J. Rodrigues, “MF-Adaboost: LDoS attack detection based on multi-features and improved Adaboost,” Future Gener. Comput. Syst., Vol. 106, pp. 347–359, 2020.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.