International Journal of Wireless and Ad Hoc Communication

Journal DOI

https://doi.org/10.54216/IJWAC

Submit Your Paper

2692-4056ISSN (Online)

Volume 6 , Issue 2 , PP: 34-42, 2023 | Cite this article as | XML | Html | PDF | Review Article

A Review on Software Fault Detection Mechanisms and Fault Prevention Mechanisms in Networks

Preeti Baderiya 1 , Chetan Gupta 2 , Shivendra Dubey 3

  • 1 M. Tech. Scholar, Department of CSE Sagar Institute of Research Technology & Science, Madhya Pradesh, India - (preeti.baderiya84@gmail.com)
  • 2 Department of CSE Sagar Institute of Research Technology & Science, Madhya Pradesh, India - (chetangupta.gupta1@gmail.com)
  • 3 Department of CSE, School of Engineering & Technology, Jagran Lakecity University, Bhopal, Madhya Pradesh, India - (shivendrashivay@gmail.com)
  • Doi: https://doi.org/10.54216/IJWAC.060203

    Received: September 18, 2022 Accepted: November 10, 2022
    Abstract

    It is possible to improve software quality by anticipating fault location through the utilization of software metrics within fault prediction models in network. This article provides a comprehensive literature review on the topic of software fault forecasting. The paper also seeks to identify software metrics and evaluate how applicable those metrics are to the process of software fault prediction. It is recommended that additional research be conducted on large industrial software systems to identify metrics that are more pertinent for the industry and to find an answer to the question of which metrics should be employed in a particular setting.

    Keywords :

    Software development , Fault Detection , Network , Fault Prevention , Software faults , Dynamic selection

    References

    [1]    S. S. Rathore and S. Kumar, “Software fault prediction based on the dynamic selection of learning technique: findings from the eclipse project study,” Appl. Intell., vol. 51, no. 12, pp. 8945–8960, 2021, doi: 10.1007/s10489-021-02346-x.

    [2]   S. S. Rathore and S. Kumar, “A study on software fault prediction techniques,” Artif. Intell. Rev., vol. 51, no. 2, pp. 255–327, 2019, doi: 10.1007/s10462-017-9563-5.

    [3]   D. Radjenović, M. Heričko, R. Torkar, and A. Živkovič, “Software fault prediction metrics: A systematic literature review,” Inf. Softw. Technol., vol. 55, no. 8, pp. 1397–1418, 2013, doi: https://doi.org/10.1016/j.infsof.2013.02.009.

    [4]   B. Dhanalaxmi, G. Apparao Naidu, and K. Anuradha, “A Review on Soware Fault Detection and Prevention Mechanism in Soware Development Activities Related papers A Review on Software Fault Detection and Prevention Mechanism in Software Development Activities,” IOSR J. Comput. Eng., vol. 17, no. 6, pp. 25–30, 2015, doi: 10.9790/0661-17652530.

    [5]   Y. LI, Y. MA, R. PENG, and K. GAO, “Prediction of Software Fault Detection and Correction Processes With Time Series Analysis,” in 2020 Asia-Pacific International Symposium on Advanced Reliability and Maintenance Modeling (APARM), 2020, pp. 1–6. doi: 10.1109/APARM49247.2020.9209402.

    [6]  D. A. A. G. Singh, A. E. Fernando, and E. J. Leavline, “Experimental study on feature selection methods for software fault detection,” in 2016 International Conference on Circuit, Power and Computing Technologies (ICCPCT), 2016, pp. 1–6. doi: 10.1109/ICCPCT.2016.7530156.

    [7]    X. Xing, J. Luo, Z. Jia, Y. Li, and Q. Han, “Automated Fault Detection for Web Services using Naïve Bayes Approach,” in 2019 IEEE 10th International Conference on Software Engineering and Service Science (ICSESS), 2019, pp. 336–339. doi: 10.1109/ICSESS47205.2019.9040756.

    [8]   Y. Wang, X. Chen, W. Zhou, X. Liu, J. Li, and G. Lu, “Using Algebra Graph Representation to Detect Pairwise-Constraint Software Faults,” IEEE Access, vol. 8, pp. 184550–184559, 2020, doi: 10.1109/ACCESS.2020.3029094.

    [9]   S. Kong, M. Lu, B. Sun, J. Ai, and S. Wang, “Detection Software Content Failures using Dynamic Execution Information,” in 2021 IEEE 21st International Conference on Software Quality, Reliability and Security Companion (QRS-C), 2021, pp. 141–147. doi: 10.1109/QRS-C55045.2021.00029.

    [10]  K. Yeon and D. Lee, “Fault detection and diagnostic coverage for the domain control units of vehicle E/E systems on functional safety,” in 2017 20th International Conference on Electrical Machines and Systems (ICEMS), 2017, pp. 1–4. doi: 10.1109/ICEMS.2017.8056361.

    [11] T. B. Alakus, R. Das, and I. Turkoglu, “An Overview of Quality Metrics Used in Estimating Software Faults,” in 2019 International Artificial Intelligence and Data Processing Symposium (IDAP), 2019, pp. 1–6. doi: 10.1109/IDAP.2019.8875925.

    [12] R. Natella and A. Andrzejak, “SAR Handbook Chapter: Experimental Tools for Software Aging Analysis,” in 2020 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW), 2020, p. 1. doi: 10.1109/ISSREW51248.2020.00096.

    [13] M. S. H. M. Izani, K. S. Muhammad, and R. Baharom, “Open Circuit Fault Tolerant Bridgeless Cuk Rectifier with Fault Detection Technique,” in 2021 IEEE Industrial Electronics and Applications Conference (IEACon), 2021, pp. 207–212. doi: 10.1109/IEACon51066.2021.9654750.

    [14] J. Zhao, G. Ning, H. Lu, Y. Wang, C. Yan, and J. Zhang, “Poster: A Weight-Based Approach to Combinatorial Test Generation,” in 2018 IEEE/ACM 40th International Conference on Software Engineering: Companion (ICSE-Companion), 2018, pp. 378–383.

    [15] Bushra Khalid, Kapil Sharma. (2015). Ranking of Software Reliability Growth Models Using Bacterial Foraging Optimization Algorithm. International Conference on Computing for Sustainable Global Development. IEEE, 1643-1648.

    [16] K. Lu and Z. Ma. Parameter Estimation of Software Reliability Growth Models by A Modified Whale Optimization Algorithm.(2018). International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES), 268-271.

    [17] L. Zhen, Y. Liu, W. Dongsheng and Z. Wei. (2020). Parameter Estimation of Software Reliability Model and Prediction Based on Hybrid Wolf Pack Algorithm and Particle Swarm Optimization. IEEE Access, 8, 29354-29369.

    [18] M. Gheisari et al. (2019). An Optimization Model for Software Quality Prediction With Case Study Analysis Using MATLAB. IEEE Access, 7, 85123-85138.

    [19] P. Prashant, A. Tickoo, S. Sharma and J. Jamil. (2019). Optimization of cost to calculate the release time in software reliability using python. International Conference on Cloud Computing, Data Science & Engineering (Confluence), 471-474.

    [20] P. Roy, G. S. Mahapatra and K. N. Dey. (2019). Forecasting of software reliability using neighborhood fuzzy particle swarm optimization based novel neural network. IEEE/CAA Journal of Automatica Sinica, 6(6), 1365-1383.

    [21]Ramakanta Mohanthy, Venkatshwarlu Naik, Azmath Mubeen. (2014). Predicting Software Reliability Using Ant Colony Optimization Technique. International Conference on Communication Systems and Network Technologies, 496-500.

    [22]R. K. Mohanty, V. Ravi, and M. R. Patra. (2013). Hybrid intelligent Systems for predicting Software reliability,” Elsevier, Applied Soft Computing, 13(1), 189-200.

    [23] Z. Li, M. Yu, D. Wang and H. Wei. (2019). Using Hybrid Algorithm to Estimate and Predicate Based on Software Reliability Model. IEEE Access, 7, 84268-84283.

    [24] Salama, A. A., Smarandache, F., &Kroumov, V., Neutrosophic crisp Sets & Neutrosophic crisp Topological Spaces. Sets and Systems, 2(1), 25-30, 2014. 

    [25] Smarandache, F. &Pramanik, S. (Eds). (2016). New trends in neutrosophic theory and applications. Brussels: Pons Editions.

    [26] Alhabib, R., The Neutrosophic Time Series, the Study of Its Linear Model, and test Significance of Its Coefficients. Albaath University Journal, Vol.42, 2020, (Arabic version). 

    [27] Kumar A, Dubey S, Arshad M, Saxena S, Sinha SK, Dixit P, Arjaria A. Enhanced Cloud Data Storage Security by Using Hadoop. InProceedings of the International Conference on Innovative Computing & Communications (ICICC) 2020 Mar 30.

     

    Cite This Article As :
    Baderiya, Preeti. , Gupta, Chetan. , Dubey, Shivendra. A Review on Software Fault Detection Mechanisms and Fault Prevention Mechanisms in Networks. International Journal of Wireless and Ad Hoc Communication, vol. , no. , 2023, pp. 34-42. DOI: https://doi.org/10.54216/IJWAC.060203
    Baderiya, P. Gupta, C. Dubey, S. (2023). A Review on Software Fault Detection Mechanisms and Fault Prevention Mechanisms in Networks. International Journal of Wireless and Ad Hoc Communication, (), 34-42. DOI: https://doi.org/10.54216/IJWAC.060203
    Baderiya, Preeti. Gupta, Chetan. Dubey, Shivendra. A Review on Software Fault Detection Mechanisms and Fault Prevention Mechanisms in Networks. International Journal of Wireless and Ad Hoc Communication , no. (2023): 34-42. DOI: https://doi.org/10.54216/IJWAC.060203
    Baderiya, P. , Gupta, C. , Dubey, S. (2023) . A Review on Software Fault Detection Mechanisms and Fault Prevention Mechanisms in Networks. International Journal of Wireless and Ad Hoc Communication , () , 34-42 . DOI: https://doi.org/10.54216/IJWAC.060203
    Baderiya P. , Gupta C. , Dubey S. [2023]. A Review on Software Fault Detection Mechanisms and Fault Prevention Mechanisms in Networks. International Journal of Wireless and Ad Hoc Communication. (): 34-42. DOI: https://doi.org/10.54216/IJWAC.060203
    Baderiya, P. Gupta, C. Dubey, S. "A Review on Software Fault Detection Mechanisms and Fault Prevention Mechanisms in Networks," International Journal of Wireless and Ad Hoc Communication, vol. , no. , pp. 34-42, 2023. DOI: https://doi.org/10.54216/IJWAC.060203