ECBFMBP: Design of an Ensemble deep learning Classifer with Bio-inspired Feature Selection for high-efciency Multidomain Bug Prediction Darshana Tambe 1,2* & Lata Ragha 3 1 Lokmanya Tilak College of Engineering, India 2 Vasantdada Patil Prathisthan’s College of Engineering, India 3 Fr. C. Rodrigues Institute of Technology, India Abstract Prediction of software bugs from process logs, temporal access logs, behavior analysis, etc. requires estimation of a wide variety of high-density feature sets. Extracted feature sets must be able to classify these logs into different bug categories with high accuracy, and low complexity. To perform these tasks, a wide variety of Machine Learning Models (MLMs) are proposed by researchers, and each of them varies in terms of their performance- level nuances, functional advantages, contextual limitations, and application-specific future scopes. Upon analyzing these characteristics, it was observed that existing models are highly context-specific, and cannot be applied to multidomain bug analysis datasets. Moreover, existing models do not incorporate a dynamic feature selection method, which limits their accuracy performance under multiple bug classification applications. To overcome these issues, this paper proposes design of a novel Ensemble deep learning Classifer with Feature Selection for high- efciency Multidomain Bug Prediction under diferent use cases. The proposed model improves bug representation performance by combining multiple feature extraction methods, including GWO-based novel feature selection techniques. The ensemble Received 22 August 2023; Revised 24 September 2023; Accepted 25 September 2023 * Correspondence: darshanatambe.phdwork@gmail.com Journal of Cognitive Science 24(3): 313-336 September 2023 ©2023 Institute for Cognitive Science, Seoul National University