Treffer: Enhancing IOT based software defect prediction in analytical data management using war strategy optimization and Kernel ELM.

The existence of software problems in IoT applications caused by insufficient source code, poor design, mistakes, and insufficient testing poses a serious risk to functioning and user expectations. Prior to software deployment, thorough testing and quality assurance methods are crucial to reducing these risks. This study advances the field of IoT-based software quality assessment while also showcasing the viability and benefits of incorporating AI methods into Software Defect Prediction (SDP), particularly the Kernel-based Extreme Learning Machine (KELM) and the War Strategy Optimisation (WSO) algorithm. These efforts are essential to maintain the dependability and performance of IoT applications given the IoT's rising significance in our linked world. The chosen keywords, such as Software defect prediction, IoT, KELM, and WSO, capture the multidimensional nature of this novel technique and serve as an important source of information for upcoming study in this area. One of the main issues that needs to be addressed in order to overcome the difficulties of developing IoT-based software is how time and resource-consuming it is to test the programme in order to ensure its effectiveness. Software Defect Prediction (SDP) assumes a crucial function in this context in locating flaws in software components. Manual defect analysis grows more inefficient and time-consuming as software projects become more complicated. This research introduces a fresh method to SDP by utilising artificial intelligence (AI) to address these issues. The suggested methodology includes the War Strategy Optimisation (WSO) algorithm, which is cleverly used to optimise classifier hyperparameters, together with a Kernel Extreme Learning Machine (KELM) for SDP. The main objective is to improve softw. This innovative combination, grounded in previous studies [1, 2], promises superior capabilities in predicting software defects. Notably, it represents the inaugural endeavor to integrate the WSO algorithm with KELM for SDP, introducing a unique and advanced approach to software quality assessment. The proposed methodology undergoes rigorous evaluation using a diverse set of real-world software project datasets, including the renowned PROMISE dataset and various open-source datasets coded in Java. Performance assessment is conducted through multiple metrics, including Efficiency Accuracy, Reliability, Sensitivity, and F1-score, collectively illuminating the effectiveness of this approach. The outcome of our experiments underscores the potency of the Kernel Extreme Learning Machine coupled with the War Strategy Optimization algorithm in enhancing the accuracy of SDP and consequently elevating defect detection efficiency within software components. Remarkably, our methodology consistently outperforms existing techniques, registering an average increase of over 90% in accuracy across the parameters examined. This promising result underscores the potential of our approach to effectively tackle the challenges associated with IoT-based software development and software defect prediction. In conclusion, this study significantly contributes to the field of IoT-based software quality assessment, introducing an innovative methodology that substantially bolsters accuracy and reliability in SDP. [ABSTRACT FROM AUTHOR]

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