{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T08:09:14Z","timestamp":1770278954973,"version":"3.49.0"},"reference-count":56,"publisher":"World Scientific Pub Co Pte Ltd","issue":"02","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Model. Simul. Sci. Comput."],"published-print":{"date-parts":[[2023,4]]},"abstract":"<jats:p> Defects are frequent in software systems, and they can cause a lot of issues for users. Despite the fact that many studies have been conducted on employing software product metrics to determine defect-prone modules, defect prediction techniques are still worth investigating. Hence, the aim of this work is to provide a unique Software Defect Prediction (SDP) approach that includes four steps like \u201c(a) pre-processing, (b) feature extraction, (c) feature selection and (d) detection.\u201d At first, the input data are given to the pre-processing step, as well as in the feature extraction step; the \u201cstatistical features, raw features, higher-order statistical features as well as proposed entropy features\u201d are extracted from the pre-processed data. In addition, the retrieved features are sent into a feature selection step, wherein the appropriate features are selected utilizing a modified chi-square scheme. In the detection step, a hybrid Long Short-Term Memory (LSTM) and Deep Belief Network (DBN) classifiers are used to predict the defects. To provide a more accurate detection, the weights of both DBN and LSTM are optimally tuned via a Self Improved Social Ski-Driver Optimization (SISSDO) algorithm. This proposed SDP model is a beneficial practice for enhancing software quality and reliability. Moreover, the results of the adopted technique are assessed to traditional techniques on the basis of various measures. In particular, the accuracy of the suggested approach for dataset 3 is 5.80%, 6.52%, 5.07%, 7.97%, 5.80%, 9.42%, 9.42%, 10.15%, 2.17%, and 3.62% better than the extant HC[Formula: see text]+[Formula: see text]ALO, HC[Formula: see text]+[Formula: see text]SMO, HC[Formula: see text]+[Formula: see text]CMBO, HC[Formula: see text]+[Formula: see text]SSD, RNN, CNN, NN, Bi-LSTM, HC+SPFCNN, and HC[Formula: see text]+[Formula: see text]CWAR approaches, correspondingly. Moreover, the computation time of the suggested approach is 17.05%, 5.78%, 1.31%, and 50.53% better than the existing HC[Formula: see text]+[Formula: see text]ALO, HC[Formula: see text]+[Formula: see text]SMO, HC[Formula: see text]+[Formula: see text]CMBO, and HC[Formula: see text]+[Formula: see text]SSD approaches, correspondingly. <\/jats:p>","DOI":"10.1142\/s1793962323500319","type":"journal-article","created":{"date-parts":[[2022,8,10]],"date-time":"2022-08-10T12:12:21Z","timestamp":1660133541000},"source":"Crossref","is-referenced-by-count":10,"title":["Hybrid model with optimization tactics for software defect prediction"],"prefix":"10.1142","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3685-0536","authenticated-orcid":false,"given":"Shantappa G.","family":"Gollagi","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, KLE Society\u2019s KLE College of Engineering and Technology, Chikodi, Karnataka, India"},{"name":"Visvesvaraya Technological University, Belagavi, Karnataka, India"}]},{"given":"S","family":"Balasubramaniam","sequence":"additional","affiliation":[{"name":"Department of Futures Studies, University of Kerala, Thiruvananthapuram, Kerala, India"}]}],"member":"219","published-online":{"date-parts":[[2022,9,24]]},"reference":[{"key":"S1793962323500319BIB001","doi-asserted-by":"crossref","first-page":"9847","DOI":"10.1007\/s10586-018-1696-z","volume":"22","author":"Manjula C.","year":"2019","journal-title":"Cluster Comput."},{"key":"S1793962323500319BIB002","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1007\/s00500-020-05159-1","volume":"25","author":"Jin C.","year":"2021","journal-title":"Soft Comput."},{"key":"S1793962323500319BIB003","doi-asserted-by":"crossref","first-page":"10925","DOI":"10.1007\/s10586-017-1235-3","volume":"22","author":"Anbu M.","year":"2019","journal-title":"Cluster Comput."},{"key":"S1793962323500319BIB004","doi-asserted-by":"crossref","first-page":"8249","DOI":"10.1007\/s00521-020-04960-1","volume":"33","author":"Wang K.","year":"2021","journal-title":"Neural Comput. 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