{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T13:31:44Z","timestamp":1760707904380},"edition-number":"1","reference-count":22,"publisher":"Wiley","isbn-type":[{"type":"print","value":"9780471383932"},{"type":"electronic","value":"9780470050118"}],"license":[{"start":{"date-parts":[[2008,1,15]],"date-time":"2008-01-15T00:00:00Z","timestamp":1200355200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>The development of effort models has been an area of considerable research in software engineering. Most early models were algorithmic and derived relationships empirically between effort and project characteristics, mostly using regression analysis. Recently, they have been complemented by machine learning and by neural net techniques. Various comparative studies have also been pursued to evaluate the predictive performance of different approaches. This article first highlights the main effort\u2010prediction techniques and comparative studies and then discusses the model development process and some relevant issues. An illustrative example is employed to develop support vector prediction models for data from several industrial projects.<\/jats:p>","DOI":"10.1002\/9780470050118.ecse706","type":"other","created":{"date-parts":[[2008,1,15]],"date-time":"2008-01-15T14:46:25Z","timestamp":1200408385000},"source":"Crossref","is-referenced-by-count":3,"title":["Software Effort Prediction"],"prefix":"10.1002","author":[{"given":"Hojung","family":"Lim","sequence":"first","affiliation":[]},{"given":"Amrit L.","family":"Goel","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2008,1,15]]},"reference":[{"key":"e_1_2_7_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.2007.256943"},{"key":"e_1_2_7_3_1","doi-asserted-by":"publisher","DOI":"10.1147\/sj.161.0054"},{"key":"e_1_2_7_4_1","unstructured":"J. W.BaileyandV. R.Basili A meta\u2010model for software development resource expenditures Proc. of the 5thInternational Conference on Software Engineering 107\u2013116.1981."},{"key":"e_1_2_7_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/32.799958"},{"key":"e_1_2_7_6_1","doi-asserted-by":"publisher","DOI":"10.1142\/5700"},{"key":"e_1_2_7_7_1","unstructured":"C.Schofield An Empirical Investigation into Software Effort Estimation by Analogy Ph.D. Dissertation. Dorset UK: Bournemouth University 1998."},{"key":"e_1_2_7_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/32.637387"},{"key":"e_1_2_7_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.1978.231521"},{"volume-title":"Software Engineering Economics","year":"1981","author":"Boehm B. W.","key":"e_1_2_7_10_1"},{"key":"e_1_2_7_11_1","unstructured":"B. W.Boehm COCOMO II Experience and Plans ESCOM97 Berlin 1997."},{"key":"e_1_2_7_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.1983.235271"},{"key":"e_1_2_7_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0950-5849(97)00004-9"},{"key":"e_1_2_7_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/32.852743"},{"key":"e_1_2_7_15_1","unstructured":"H.Lim Support Vector Parameter Selection Using Experimental Design Based Generating Set Search (SVEG) with Applications to Predictive Software Data Modeling Ph.D Dissertation Syracuse N. Y. Syracuse University 2004."},{"key":"e_1_2_7_16_1","doi-asserted-by":"crossref","unstructured":"L.Briand et al.A replicated assessment and comparison of common software cost modeling techniques Interna. Conf. Software Engineering 377\u2013386 2000.","DOI":"10.1145\/337180.337223"},{"key":"e_1_2_7_17_1","unstructured":"J.Dolado Limits to methods in cost estimation Technical Report Spain:University of Vasque County 1999."},{"key":"e_1_2_7_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/22899.22906"},{"volume-title":"Masters Thesis","year":"1988","author":"Desharnais J. M.","key":"e_1_2_7_19_1"},{"key":"e_1_2_7_20_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-84628-288-1_53"},{"volume-title":"Statistical Learning Theory","year":"1998","author":"Vapnik V. N.","key":"e_1_2_7_21_1"},{"volume-title":"Neural Networks \u2013 A Comprehensive Foundation","year":"1999","author":"Haykin S.","key":"e_1_2_7_22_1"},{"key":"e_1_2_7_23_1","unstructured":"R. M.Rifkin Everything Old is New Again: A Fresh Look at Historical Approaches in Machine Learning. Ph.D. Dissertation Cambridge MA: Massachusetts Institute of Technology 2002."}],"container-title":["Wiley Encyclopedia of Computer Science and Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/9780470050118.ecse706","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,21]],"date-time":"2024-02-21T08:26:27Z","timestamp":1708503987000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/9780470050118.ecse706"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2008,1,15]]},"ISBN":["9780471383932","9780470050118"],"references-count":22,"alternative-id":["10.1002\/9780470050118.ecse706","10.1002\/9780470050118"],"URL":"https:\/\/doi.org\/10.1002\/9780470050118.ecse706","archive":["Portico"],"relation":{},"subject":[],"published":{"date-parts":[[2008,1,15]]}}}