International Journal of Advances in Applied Computational Intelligence IJAACI 2833-5600 10.54216/IJAACI https://www.americaspg.com/journals/show/3165 2022 2022 Information Management School, Universidade Nova de Lisboa, 1070-312, Lisboa, Portugal Ahmed Ahmed Information Management School, Universidade Nova de Lisboa, 1070-312, Lisboa, Portugal Alia N. Mahmoud Nova 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. 2024 2024 83 91 10.54216/IJAACI.060208 https://www.americaspg.com/articleinfo/31/show/3165