International Journal of Advances in Applied Computational Intelligence
Journal DOI
2833-5600ISSN (Online)
Volume 6 , Issue 2 , PP: 83-91, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
Ahmed Abdelaziz 1 * , Alia N. Mahmoud Nova 2
Doi: https://doi.org/10.54216/IJAACI.060208
Software defect prediction is a technique that may foretell when and where software errors will manifest. It should be the aim of every software development project to provide a product devoid of bugs. Software defect prediction (SDP) is a crucial aspect of software repair that involves predicting potential code locations for problems. Software of excellent quality need to be bug-free. Software metrics are assessments of the program or its needs that are either quantitative or qualitative in nature. The Lévy flying patterns of various birds and fruit flies, together with the flight patterns of some cuckoo species, served as inspiration for Cuckoo Search (CS), a population-based algorithm that was developed relatively recently. Computer science satisfies the requirements for global convergence. Among the many supervised learning methods that do not need parameters, KNN stands out. This study provides a social metaphorical overview of Stochastic Diffusion Search (SDS) to show how SDS distributes resources. Using a new probabilistic approach, SDS solved the problems of best-fit pattern recognition and matching. Using interactions amongst basic agents, SDS is a distributed computing paradigm that employs multiagent population-based global search and optimization. An optimization approach that combines CS and SDS methods is suggested in this work. This hybridization proposal seeks to improve the cuckoo bird's search strategy for the ideal host nest by using the global search strategy solution of the SDS algorithm. So, to find the best spot for the cuckoo egg, the SDS approach would be used. One possible explanation for PC2's superior performance when compared to other classifiers is its greater recall values. Specifically, KNN outperforms Radial Bias Neural Network (2.20% improvement) and Naive Bayes (7.54% improvement) classifiers.
Stochastic Diffusion Search , Cuckoo Search , Software Defect Prediction , K Nearest Neighbor , Naï , ve Bayes , Radial Bias Neural Network
[1] Liu, S., Zhang, Y., Liu, Y., Wang, L., & Wang, X. V. (2019). An ‘Internet of Things’ enabled dynamic optimization method for smart vehicles and logistics tasks. Journal of cleaner production, 215, 806-820.
[2] Liebig, T., Piatkowski, N., Bockermann, C., & Morik, K. (2017). Dynamic route planning with real-time traffic predictions. Information Systems, 64, 258-265.
[3] Wang, M., Shan, H., Lu, R., Zhang, R., Shen, X., & Bai, F. (2014). Real-time path planning based on hybrid-VANET-enhanced transportation system. IEEE Transactions on vehicular technology, 64(5), 1664-1678.
[4] De Souza, A. M., Yokoyama, R. S., Maia, G., Loureiro, A., & Villas, L. (2016, June). Real-time path planning to prevent traffic jam through an intelligent transportation system. In 2016 IEEE symposium on computers and communication (ISCC) (pp. 726-731). IEEE.
[5] Kim, S., Lewis, M. E., & White, C. C. (2005). Optimal vehicle routing with real-time traffic information. IEEE Transactions on Intelligent Transportation Systems, 6(2), 178-188.
[6] Surinder Kaur , Shivani Mankotia , Pooja Bharadwaj, Study of Multi-Prime RSA, Fusion: Practice and Applications, Vol. 1 , No. 1 , (2020) : 40-48 (Doi : https://doi.org/10.54216/FPA.010105)
[7] Harsh Jain , Parv Bharti , Arun Kumar Dubey , Preetika Soni, Identification of Facial Expressions using Deep Neural Networks, Fusion: Practice and Applications, Vol. 2 , No. 1 , (2020) : 22-30 (Doi : https://doi.org/10.54216/FPA.020101)
[8] Shaymaa Adnan Abdulrahma , Abdel-Badeeh M. Salem, An efficient deep belief network for Detection of Coronavirus Disease COVID-19, Fusion: Practice and Applications, Vol. 2 , No. 1 , (2020) : 05-13 (Doi : https://doi.org/10.54216/FPA.020102)
[9] Ehmke, J. (2012). Integration of information and optimization models for routing in city logistics (Vol. 177). Springer Science & Business Media.
[10] Mondragon, A. E. C., Lalwani, C. S., Mondragon, E. S. C., Mondragon, C. E. C., & Pawar, K. S. (2012). Intelligent transport systems in multimodal logistics: A case of role and contribution through wireless vehicular networks in a sea port location. International Journal of Production Economics, 137(1), 165-175.
[11] Fleischmann, B., Gnutzmann, S., & Sandvoß, E. (2004). Dynamic vehicle routing based on online traffic information. Transportation science, 38(4), 420-433.
[12] Lin, J., Yu, W., Yang, X., Yang, Q., Fu, X., & Zhao, W. (2016). A real-time en-route route guidance decision scheme for transportation-based cyberphysical systems. IEEE Transactions on Vehicular Technology, 66(3), 2551-2566.
[13] Güner, A. R., Murat, A., & Chinnam, R. B. (2012). Dynamic routing under recurrent and non-recurrent congestion using real-time ITS information. Computers & Operations Research, 39(2), 358-373.
[14] Kumar, P. M., Manogaran, G., Sundarasekar, R., Chilamkurti, N., & Varatharajan, R. (2018). Ant colony optimization algorithm with internet of vehicles for intelligent traffic control system. Computer Networks, 144, 154-162.
[15] Mahmassani, H. S. (2001). Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and spatial economics, 1, 267-292.
[16] Zhu, F., Lv, Y., Chen, Y., Wang, X., Xiong, G., & Wang, F. Y. (2019). Parallel transportation systems: Toward IoT-enabled smart urban traffic control and management. IEEE Transactions on Intelligent Transportation Systems, 21(10), 4063-4071.
[17] Du, R., Chen, S., Dong, J., Ha, P. Y. J., & Labi, S. (2021, September). GAQ-EBkSP: a DRL-based urban traffic dynamic rerouting framework using fog-cloud architecture. In 2021 IEEE International Smart Cities Conference (ISC2) (pp. 1-7). IEEE.
[18] Cao, Z., Jiang, S., Zhang, J., & Guo, H. (2016). A unified framework for vehicle rerouting and traffic light control to reduce traffic congestion. IEEE transactions on intelligent transportation systems, 18(7), 1958-1973.
[19] Huang, H. Y., Luo, P. E., Li, M., Li, D., Li, X., Shu, W., & Wu, M. Y. (2007). Performance evaluation of SUVnet with real-time traffic data. IEEE Transactions on Vehicular Technology, 56(6), 3381-3396.
[20] Kuru, K., Ansell, D., Khan, W., & Yetgin, H. (2019). Analysis and optimization of unmanned aerial vehicle swarms in logistics: An intelligent delivery platform. Ieee Access, 7, 15804-15831.
