{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,25]],"date-time":"2025-05-25T09:21:53Z","timestamp":1748164913336},"reference-count":16,"publisher":"World Scientific Pub Co Pte Ltd","issue":"03","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Neur. Syst."],"published-print":{"date-parts":[[2004,6]]},"abstract":"<jats:p>A recurrent neural network modeling approach for software reliability prediction with respect to cumulative failure time is proposed. Our proposed network structure has the capability of learning and recognizing the inherent internal temporal property of cumulative failure time sequence. Further, by adding a penalty term of sum of network connection weights, Bayesian regularization is applied to our network training scheme to improve the generalization capability and lower the susceptibility of overfitting. The performance of our proposed approach has been tested using four real-time control and flight dynamic application data sets. Numerical results show that our proposed approach is robust across different software projects, and has a better performance with respect to both goodness-of-fit and next-step-predictability compared to existing neural network models for failure time prediction.<\/jats:p>","DOI":"10.1142\/s0129065704001966","type":"journal-article","created":{"date-parts":[[2004,7,6]],"date-time":"2004-07-06T09:27:27Z","timestamp":1089106047000},"page":"165-174","source":"Crossref","is-referenced-by-count":14,"title":["SOFTWARE RELIABILITY PREDICTION USING RECURRENT NEURAL NETWORK WITH BAYESIAN REGULARIZATION"],"prefix":"10.1142","volume":"14","author":[{"given":"LIANG","family":"TIAN","sequence":"first","affiliation":[{"name":"Lane Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, WV 26506\u20136109, USA"}]},{"given":"AFZEL","family":"NOORE","sequence":"additional","affiliation":[{"name":"Lane Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, WV 26506\u20136109, USA"}]}],"member":"219","published-online":{"date-parts":[[2011,11,21]]},"reference":[{"key":"rf1","doi-asserted-by":"publisher","DOI":"10.1016\/0951-8320(91)90009-V"},{"key":"rf2","doi-asserted-by":"publisher","DOI":"10.1109\/32.148475"},{"key":"rf3","doi-asserted-by":"publisher","DOI":"10.1109\/52.143107"},{"key":"rf4","first-page":"533","volume":"4","author":"Park J. Y.","journal-title":"J. Electrical Engineering and Information Science"},{"key":"rf5","doi-asserted-by":"publisher","DOI":"10.1016\/S0164-1212(01)00027-9"},{"key":"rf6","doi-asserted-by":"publisher","DOI":"10.1142\/S0218539302000780"},{"key":"rf7","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2002.804317"},{"key":"rf10","doi-asserted-by":"publisher","DOI":"10.1109\/24.799900"},{"key":"rf11","doi-asserted-by":"publisher","DOI":"10.1016\/S0898-1221(03)90117-9"},{"key":"rf14","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2003.814470"},{"key":"rf15","doi-asserted-by":"publisher","DOI":"10.1207\/s15516709cog1402_1"},{"key":"rf16","doi-asserted-by":"publisher","DOI":"10.1109\/72.935086"},{"key":"rf17","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1992.4.3.415"},{"key":"rf21","doi-asserted-by":"publisher","DOI":"10.1002\/nag.291"},{"key":"rf22","doi-asserted-by":"crossref","unstructured":"L. H.\u00a0Tsoukalas and R. E.\u00a0Uhrig, Fuzzy and Neural Approaches in Engineering (John Wiley & Sons, New York, 1996)\u00a0pp. 385\u2013405.","DOI":"10.1007\/978-94-011-0305-3_13"},{"key":"rf23","series-title":"McGraw-Hill Series in Software Engineering and Technology","volume-title":"Software Reliability: Measurement, Prediction, Application","author":"Musa J. D.","year":"1987"}],"container-title":["International Journal of Neural Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.worldscientific.com\/doi\/pdf\/10.1142\/S0129065704001966","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,4,28]],"date-time":"2023-04-28T23:12:28Z","timestamp":1682723548000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.worldscientific.com\/doi\/abs\/10.1142\/S0129065704001966"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2004,6]]},"references-count":16,"journal-issue":{"issue":"03","published-online":{"date-parts":[[2011,11,21]]},"published-print":{"date-parts":[[2004,6]]}},"alternative-id":["10.1142\/S0129065704001966"],"URL":"https:\/\/doi.org\/10.1142\/s0129065704001966","relation":{},"ISSN":["0129-0657","1793-6462"],"issn-type":[{"value":"0129-0657","type":"print"},{"value":"1793-6462","type":"electronic"}],"subject":[],"published":{"date-parts":[[2004,6]]}}}