{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T03:00:51Z","timestamp":1778209251440,"version":"3.51.4"},"reference-count":43,"publisher":"Emerald","issue":"2","license":[{"start":{"date-parts":[[2024,3,22]],"date-time":"2024-03-22T00:00:00Z","timestamp":1711065600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJICC"],"published-print":{"date-parts":[[2024,5,30]]},"abstract":"<jats:sec><jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title><jats:p>Software defect prediction (SDP) is a critical aspect of software quality assurance, aiming to identify and manage potential defects in software systems. In this paper, we have proposed a novel hybrid approach that combines Grey Wolf Optimization with Feature Selection (GWOFS) and multilayer perceptron (MLP) for SDP. The GWOFS-MLP hybrid model is designed to optimize feature selection, ultimately enhancing the accuracy and efficiency of SDP. Grey Wolf Optimization, inspired by the social hierarchy and hunting behavior of grey wolves, is employed to select a subset of relevant features from an extensive pool of potential predictors. This study investigates the key challenges that traditional SDP approaches encounter and proposes promising solutions to overcome time complexity and the curse of the dimensionality reduction problem.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title><jats:p>The integration of GWOFS and MLP results in a robust hybrid model that can adapt to diverse software datasets. This feature selection process harnesses the cooperative hunting behavior of wolves, allowing for the exploration of critical feature combinations. The selected features are then fed into an MLP, a powerful artificial neural network (ANN) known for its capability to learn intricate patterns within software metrics. MLP serves as the predictive engine, utilizing the curated feature set to model and classify software defects accurately.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Findings<\/jats:title><jats:p>The performance evaluation of the GWOFS-MLP hybrid model on a real-world software defect dataset demonstrates its effectiveness. The model achieves a remarkable training accuracy of 97.69% and a testing accuracy of 97.99%. Additionally, the receiver operating characteristic area under the curve (ROC-AUC) score of 0.89 highlights the model\u2019s ability to discriminate between defective and defect-free software components.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title><jats:p>Experimental implementations using machine learning-based techniques with feature reduction are conducted to validate the proposed solutions. The goal is to enhance SDP\u2019s accuracy, relevance and efficiency, ultimately improving software quality assurance processes. The confusion matrix further illustrates the model\u2019s performance, with only a small number of false positives and false negatives.<\/jats:p><\/jats:sec>","DOI":"10.1108\/ijicc-11-2023-0385","type":"journal-article","created":{"date-parts":[[2024,3,21]],"date-time":"2024-03-21T09:44:05Z","timestamp":1711014245000},"page":"436-464","source":"Crossref","is-referenced-by-count":25,"title":["A hybrid approach for optimizing software defect prediction using a\u00a0grey wolf optimization and multilayer perceptron"],"prefix":"10.1108","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5055-5969","authenticated-orcid":false,"given":"Mohd","family":"Mustaqeem","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9969-6110","authenticated-orcid":false,"given":"Suhel","family":"Mustajab","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0668-9796","authenticated-orcid":false,"given":"Mahfooz","family":"Alam","sequence":"additional","affiliation":[]}],"member":"140","published-online":{"date-parts":[[2024,3,22]]},"reference":[{"issue":"2","key":"key2024061211540890800_ref001","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1108\/ijpcc-10-2019-0081","article-title":"Resource-aware load balancing model for batch of tasks (BoT) with best fit migration policy on heterogeneous distributed computing systems","volume":"16","year":"2020","journal-title":"International Journal of Pervasive Computing and Communications"},{"issue":"3","key":"key2024061211540890800_ref002","doi-asserted-by":"publisher","first-page":"271","DOI":"10.1108\/ijpcc-04-2020-0029","article-title":"Efficient task scheduling on virtual machine in cloud computing environment","volume":"17","year":"2021","journal-title":"International Journal of Pervasive Computing and Communications"},{"issue":"20","key":"key2024061211540890800_ref003","doi-asserted-by":"publisher","first-page":"9979","DOI":"10.1007\/s00500-018-3553-7","article-title":"Software requirement optimization using a fuzzy artificial chemical reaction optimization algorithm","volume":"23","year":"2019","journal-title":"Soft Computing"},{"issue":"3","key":"key2024061211540890800_ref004","doi-asserted-by":"publisher","first-page":"363","DOI":"10.1007\/s00766-020-00328-y","article-title":"Parallel multi-objective artificial bee colony algorithm for software requirement optimization","volume":"25","year":"2020","journal-title":"Requirements Engineering"},{"key":"key2024061211540890800_ref005","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1016\/j.asoc.2015.04.045","article-title":"Software defect prediction using cost-sensitive neural network","volume":"33","year":"2015","journal-title":"Applied Soft Computing"},{"issue":"5","key":"key2024061211540890800_ref006","doi-asserted-by":"publisher","first-page":"e5478","DOI":"10.1002\/cpe.5478","article-title":"An under-sampled software defect prediction method based on hybrid multi\u2010objective cuckoo search","volume":"32","year":"2020","journal-title":"Concurrency and Computation: Practice and Experience"},{"issue":"8","key":"key2024061211540890800_ref007","doi-asserted-by":"publisher","first-page":"1040","DOI":"10.1016\/j.ins.2008.12.001","article-title":"Investigating the effect of dataset size, metrics sets, and feature selection techniques on software fault prediction problem","volume":"179","year":"2009","journal-title":"Information Sciences"},{"issue":"6","key":"key2024061211540890800_ref008","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1109\/ms.2005.151","article-title":"Finding the right data for software cost modeling","volume":"22","year":"2005","journal-title":"IEEE Software"},{"key":"key2024061211540890800_ref009","first-page":"563","article-title":"Research on software defect prediction based on data mining","year":"2010"},{"key":"key2024061211540890800_ref010","first-page":"31","article-title":"An extensive comparison of bug prediction approaches","year":"2010"},{"key":"key2024061211540890800_ref011","first-page":"84","article-title":"An evolutionary immune network for data clustering","year":"2000"},{"key":"key2024061211540890800_ref012","first-page":"699","article-title":"An artificial immune network for multimodal function optimization","year":"2002"},{"issue":"5","key":"key2024061211540890800_ref013","doi-asserted-by":"publisher","first-page":"579","DOI":"10.1002\/spe.1043","article-title":"Choosing software metrics for defect prediction: an investigation on feature selection techniques","volume":"41","year":"2011","journal-title":"Software: Practice and Experience"},{"issue":"3","key":"key2024061211540890800_ref014","doi-asserted-by":"publisher","first-page":"2023","DOI":"10.1007\/s10462-021-10044-w","article-title":"Handling class-imbalance with KNN (neighbourhood) under-sampling for software defect prediction","volume":"55","year":"2022","journal-title":"Artificial Intelligence Review"},{"key":"key2024061211540890800_ref015","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2021\/6662932","article-title":"Impact of parameter tuning for optimizing deep neural network models for predicting software faults","volume":"2021","year":"2021","journal-title":"Scientific Programming"},{"issue":"1","key":"key2024061211540890800_ref016","doi-asserted-by":"publisher","first-page":"18","DOI":"10.5815\/ijmecs.2020.01.03","article-title":"A classification framework for software defect prediction using multi-filter feature selection technique and MLP","volume":"12","year":"2020","journal-title":"International Journal of Modern Education and Computer Science"},{"issue":"5","key":"key2024061211540890800_ref017","doi-asserted-by":"publisher","DOI":"10.14569\/ijacsa.2019.0100538","article-title":"Performance analysis of machine learning techniques on software defect prediction using NASA datasets","volume":"10","year":"2019","journal-title":"International Journal of Advanced Computer Science and Applications"},{"key":"key2024061211540890800_ref025","unstructured":"Isaacs, M., Karuppiah, P. and Kanitkar, M., \u2018World Quality Report\u2019 (2019-20), available at: https:\/\/www.capgemini.com\/es-es\/wp-content\/uploads\/sites\/16\/2019\/10\/World-Quality-Report-2019-20.pdf"},{"issue":"S1","key":"key2024061211540890800_ref018","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1007\/s10586-018-1730-1","article-title":"Software defect prediction techniques using metrics based on neural network classifier","volume":"22","year":"2019","journal-title":"Cluster Computing"},{"issue":"5","key":"key2024061211540890800_ref019","doi-asserted-by":"publisher","first-page":"2718","DOI":"10.3906\/elk-2001-33","article-title":"A fuzzy neural network for web service selection aimed at dynamic software rejuvenation","volume":"28","year":"2020","journal-title":"Turkish Journal of Electrical Engineering and Computer Sciences"},{"issue":"01","key":"key2024061211540890800_ref020","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1142\/s0218539309003307","article-title":"Attribute selection using rough sets in software quality classification","volume":"16","year":"2009","journal-title":"International Journal of Reliability, Quality and Safety Engineering"},{"key":"key2024061211540890800_ref021","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2016\/2401496","article-title":"Cost-sensitive radial basis function neural network classifier for software defect prediction","volume":"2016","year":"2016","journal-title":"The Scientific World Journal"},{"key":"key2024061211540890800_ref022","first-page":"318","article-title":"Software defect prediction via convolutional neural network","year":"2017"},{"issue":"4","key":"key2024061211540890800_ref023","doi-asserted-by":"publisher","first-page":"823","DOI":"10.1108\/ijicc-03-2023-0039","article-title":"Interval multi-objective grey wolf optimization algorithm based on fuzzy system","volume":"16","year":"2023","journal-title":"International Journal of Intelligent Computing and Cybernetics"},{"issue":"6","key":"key2024061211540890800_ref024","doi-asserted-by":"publisher","first-page":"e318","DOI":"10.1002\/spy2.318","article-title":"A comprehensive study on cybersecurity challenges and opportunities in the IoT world","volume":"6","year":"2023","journal-title":"Security and Privacy"},{"key":"key2024061211540890800_ref026","doi-asserted-by":"publisher","first-page":"9847","DOI":"10.1007\/s10586-018-1696-z","article-title":"Deep neural network based hybrid approach for software defect prediction using software metrics","volume":"22","year":"2019","journal-title":"Cluster Computing"},{"issue":"3","key":"key2024061211540890800_ref027","doi-asserted-by":"publisher","first-page":"2581","DOI":"10.1007\/s10586-021-03282-8","article-title":"Principal component based support vector machine (PC-SVM): a\u00a0hybrid technique for software defect detection","volume":"24","year":"2021","journal-title":"Cluster Computing"},{"issue":"1","key":"key2024061211540890800_ref028","doi-asserted-by":"publisher","first-page":"559","DOI":"10.32629\/jai.v6i1.559","article-title":"Original Research Article A hybrid software defects prediction model for imbalance datasets us-ing machine learning techniques:(S-SVM model)","volume":"6","year":"2023","journal-title":"Journal of Autonomous Intelligence"},{"key":"key2024061211540890800_ref029","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2019.113085","article-title":"BPDET: an effective software bug prediction model using deep representation and ensemble learning techniques","volume":"144","year":"2020","journal-title":"Expert Systems with Applications"},{"issue":"3","key":"key2024061211540890800_ref030","doi-asserted-by":"publisher","first-page":"5027","DOI":"10.3233\/jifs-201759","article-title":"A novel approach for the next software release using a binary artificial algae algorithm","volume":"40","year":"2021","journal-title":"Journal of Intelligent and Fuzzy Systems"},{"key":"key2024061211540890800_ref031","doi-asserted-by":"publisher","DOI":"10.1108\/ijicc-07-2023-0174","article-title":"Evaluation of predicted fault tolerance based on C5. 0 decision tree algorithm in irrigation system of paddy fields","year":"2023","journal-title":"International Journal of Intelligent Computing and Cybernetics"},{"issue":"1","key":"key2024061211540890800_ref032","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1007\/s12530-018-9261-9","article-title":"A novel under sampling strategy for efficient software defect analysis of skewed distributed data","volume":"11","year":"2020","journal-title":"Evolution Systems"},{"issue":"8","key":"key2024061211540890800_ref033","doi-asserted-by":"publisher","first-page":"4131","DOI":"10.1007\/s41870-023-01528-9","article-title":"Performance evaluation of software defect prediction with NASA dataset using machine learning techniques","volume":"15","year":"2023","journal-title":"International Journal of Information Technology"},{"key":"key2024061211540890800_ref034","first-page":"321","article-title":"Automated parameter optimization of classification techniques for defect prediction models","year":"2016"},{"key":"key2024061211540890800_ref035","first-page":"89","article-title":"A review of software defect prediction models","year":"2019"},{"issue":"4","key":"key2024061211540890800_ref036","first-page":"c3","article-title":"Software engineering glossary","volume":"20","year":"2003","journal-title":"IEEE Software"},{"issue":"1","key":"key2024061211540890800_ref037","first-page":"1","article-title":"A systematic literature review of software defect prediction","volume":"1","year":"2015","journal-title":"Journal of Software Engineering"},{"issue":"10-11","key":"key2024061211540890800_ref038","doi-asserted-by":"publisher","first-page":"1951","DOI":"10.1166\/asl.2014.5641","article-title":"Neural network parameter optimization based on genetic algorithm for software defect prediction","volume":"20","year":"2014","journal-title":"Advanced Science Letters"},{"key":"key2024061211540890800_ref039","first-page":"499","article-title":"A comparative study of threshold-based feature selection techniques","year":"2010"},{"key":"key2024061211540890800_ref040","article-title":"How many software metrics should be selected for defect prediction?","year":"2011"},{"key":"key2024061211540890800_ref041","first-page":"297","article-title":"Automatically learning semantic features for defect prediction","year":"2016"},{"key":"key2024061211540890800_ref042","first-page":"309","article-title":"The impact of feature selection on defect prediction performance: an empirical comparison","year":"2016"},{"key":"key2024061211540890800_ref043","doi-asserted-by":"publisher","first-page":"182","DOI":"10.1016\/j.infsof.2018.10.004","article-title":"Software defect prediction based on kernel PCA and weighted extreme learning machine","volume":"106","year":"2019","journal-title":"Information and Software Technology"}],"container-title":["International Journal of Intelligent Computing and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/IJICC-11-2023-0385\/full\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/IJICC-11-2023-0385\/full\/html","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,24]],"date-time":"2025-07-24T22:55:07Z","timestamp":1753397707000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.emerald.com\/ijicc\/article\/17\/2\/436-464\/1232967"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,22]]},"references-count":43,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2024,3,22]]},"published-print":{"date-parts":[[2024,5,30]]}},"alternative-id":["10.1108\/IJICC-11-2023-0385"],"URL":"https:\/\/doi.org\/10.1108\/ijicc-11-2023-0385","relation":{},"ISSN":["1756-378X"],"issn-type":[{"value":"1756-378X","type":"print"}],"subject":[],"published":{"date-parts":[[2024,3,22]]}}}