{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,17]],"date-time":"2026-05-17T20:06:48Z","timestamp":1779048408466,"version":"3.51.4"},"reference-count":26,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2011,8,4]],"date-time":"2011-08-04T00:00:00Z","timestamp":1312416000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["SIGSOFT Softw. Eng. Notes"],"published-print":{"date-parts":[[2011,8,4]]},"abstract":"<jats:p>Fairly accurate cost and effort predictions of software projects have always been a challenging goal for both, industry as well as academia. Most approaches for effort estimation are either algorithmic or analogy based. The most well-known algorithmic models are COCOMO 81 [1] and Function Points [2]. Estimation by analogy, on the other hand, is essentially a form of case based reasoning. Fuzzy logic, Grey System Theory, Machine Learning techniques such as Genetic Algorithms, Support vector Machines, etc. have been used to optimize the prediction by analogy method. We study these analogy based approaches to software effort estimation and compare some of these techniques on the basis of widely used measures of accuracy.<\/jats:p>","DOI":"10.1145\/1988997.1989016","type":"journal-article","created":{"date-parts":[[2011,8,10]],"date-time":"2011-08-10T12:16:22Z","timestamp":1312978582000},"page":"1-5","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":8,"title":["Recent methods for software effort estimation by analogy"],"prefix":"10.1145","volume":"36","author":[{"given":"Syona","family":"Gupta","sequence":"first","affiliation":[{"name":"Dr. B. R. Ambedkar National, Institute of Technology, Jalandhar, Punjab, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Geeta","family":"Sikka","sequence":"additional","affiliation":[{"name":"Dr. B. R. Ambedkar National, Institute of Technology, Jalandhar, Punjab, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Harsh","family":"Verma","sequence":"additional","affiliation":[{"name":"Dr. B. R. Ambedkar National, Institute of Technology, Jalandhar, Punjab, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2011,8,4]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","unstructured":"B. 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Bener \"Software effort estimation using machine learning methods \" 22nd international symposium on Computer and information sciences pp.1--6 2007.","DOI":"10.1109\/ISCIS.2007.4456863"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0019-9958(65)90241-X"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.5555\/823457.824043"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jss.2010.09.028"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/METRICS.2005.51"},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0167-6911(82)80025-X"},{"key":"e_1_2_1_14_1","first-page":"1470","volume-title":"2007 International Conference on","volume":"3","author":"Wang Y.","year":"2007","unstructured":"Y. Wang, Q. B. Song, J. Y. Shen, \"Grey Learning Based Software Stage-Effort Estimation,\" Machine Learning and Cybernetics, 2007 International Conference on, Vol.3, pp. 1470--1475, 2007."},{"key":"e_1_2_1_15_1","doi-asserted-by":"crossref","unstructured":"C. J. Hsu and C. Y. Huang \"Comparison and Assessment of Improved Grey Relation Analysis for Software Development Effort Estimation \" Proceedings of the 3rd International Conference on Management of Innovation and Technology (ICMIT'06) pp. 663--667 2006.","DOI":"10.1109\/ICMIT.2006.262302"},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/APSEC.2007.63"},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10664-009-9113-0"},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0164-1212(00)00005-4"},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.infsof.2005.12.020"},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1009897800559"},{"key":"e_1_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.2008.34"},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10664-006-7552-4"},{"key":"e_1_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2010.12.005"},{"key":"e_1_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10664-010-9128-6"},{"key":"e_1_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejor.2007.07.002"},{"key":"e_1_2_1_26_1","volume-title":"An algorithm for determining the number of neighbors in cost estimation by analogy using distance distributions\", 3d Artificial Intelligence Techniques in Software Engineering Workshop","author":"Kosti M. 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