{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T23:42:33Z","timestamp":1767915753813,"version":"3.49.0"},"publisher-location":"New York, NY, USA","reference-count":38,"publisher":"ACM","license":[{"start":{"date-parts":[[2020,11,8]],"date-time":"2020-11-08T00:00:00Z","timestamp":1604793600000},"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":[],"published-print":{"date-parts":[[2020,11,8]]},"DOI":"10.1145\/3416508.3417121","type":"proceedings-article","created":{"date-parts":[[2020,11,6]],"date-time":"2020-11-06T23:02:14Z","timestamp":1604703734000},"page":"31-40","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":27,"title":["Evaluating hyper-parameter tuning using random search in support vector machines for software effort estimation"],"prefix":"10.1145","author":[{"given":"Leonardo","family":"Villalobos-Arias","sequence":"first","affiliation":[{"name":"University of Costa Rica, Costa Rica"}]},{"given":"Christian","family":"Quesada-L\u00f3pez","sequence":"additional","affiliation":[{"name":"University of Costa Rica, Costa Rica"}]},{"given":"Jose","family":"Guevara-Coto","sequence":"additional","affiliation":[{"name":"University of Costa Rica, Costa Rica"}]},{"given":"Alexandra","family":"Mart\u00ednez","sequence":"additional","affiliation":[{"name":"University of Costa Rica, Costa Rica"}]},{"given":"Marcelo","family":"Jenkins","sequence":"additional","affiliation":[{"name":"University of Costa Rica, Costa Rica"}]}],"member":"320","published-online":{"date-parts":[[2020,11,8]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"How to\" DODGE\" Complex Software Analytics","author":"Agrawal Amritanshu","year":"2019","unstructured":"Amritanshu Agrawal, Wei Fu, Di Chen, Xipeng Shen, and Tim Menzies. 2019. How to\" DODGE\" Complex Software Analytics. IEEE Transactions on Software Engineering ( 2019 )."},{"key":"e_1_3_2_1_2_1","volume-title":"Machine learning with python cookbook: Practical solutions from preprocessing to deep learning. \" O'Reilly Media","author":"Albon Chris","unstructured":"Chris Albon. 2018. Machine learning with python cookbook: Practical solutions from preprocessing to deep learning. \" O'Reilly Media, Inc.\"."},{"key":"e_1_3_2_1_3_1","article-title":"Random search for hyper-parameter optimization","author":"Bergstra James","year":"2012","unstructured":"James Bergstra and Yoshua Bengio. 2012. Random search for hyper-parameter optimization. Journal of machine learning research 13, Feb ( 2012 ), 281-305.","journal-title":"Journal of machine learning research 13"},{"key":"e_1_3_2_1_4_1","unstructured":"James S Bergstra R\u00e9mi Bardenet Yoshua Bengio and Bal\u00e1zs K\u00e9gl. 2011. Algorithms for hyper-parameter optimization. In Advances in neural information processing systems. 2546-2554."},{"key":"e_1_3_2_1_5_1","first-page":"154","volume-title":"Proceedings. 5th International Workshop on Enterprise Networking and Computing in Healthcare Industry (IEEE Cat. No. 03EX717)","author":"Cartwright Michelle H","year":"2004","unstructured":"Michelle H Cartwright, Martin J Shepperd, and Qinbao Song. 2004. Dealing with missing software project data. In Proceedings. 5th International Workshop on Enterprise Networking and Computing in Healthcare Industry (IEEE Cat. No. 03EX717). IEEE, 154-165."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"crossref","unstructured":"Jacob Cohen. 1992. A power primer. Psychological bulletin 112 1 ( 1992 ) 155.","DOI":"10.1037\/\/0033-2909.112.1.155"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/1868328.1868335"},{"key":"e_1_3_2_1_8_1","volume-title":"Filomena Ferrucci, Carmine Gravino, Federica Sarro, and Emilia Mendes.","author":"Corazza Anna","year":"2013","unstructured":"Anna Corazza, Sergio Di Martino, Filomena Ferrucci, Carmine Gravino, Federica Sarro, and Emilia Mendes. 2013. Using tabu search to configure support vector regression for efort estimation. Empirical Software Engineering 18, 3 ( 2013 ), 506-546."},{"key":"e_1_3_2_1_9_1","volume-title":"Data mining techniques for software efort estimation: a comparative study","author":"Dejaeger Karel","year":"2011","unstructured":"Karel Dejaeger, Wouter Verbeke, David Martens, and Bart Baesens. 2011. Data mining techniques for software efort estimation: a comparative study. IEEE transactions on software engineering 38, 2 ( 2011 ), 375-397."},{"key":"e_1_3_2_1_10_1","volume-title":"COSMIC Function Points: Theory and Advanced Practices","author":"Dumke Reiner","unstructured":"Reiner Dumke and Alain Abran. 2016. COSMIC Function Points: Theory and Advanced Practices. CRC Press."},{"key":"e_1_3_2_1_11_1","article-title":"Performance tuning for machine learning-based software development efort prediction models","volume":"27","author":"Ertu\u011frul Egemen","year":"2019","unstructured":"Egemen Ertu\u011frul, Zakir Baytar, \u00c7a\u011fatay \u00c7atal, and \u00d6mer Can Muratli. 2019. Performance tuning for machine learning-based software development efort prediction models. Turkish Journal of Electrical Engineering & Computer Sciences 27, 2 ( 2019 ), 1308-1324.","journal-title":"Turkish Journal of Electrical Engineering & Computer Sciences"},{"key":"e_1_3_2_1_12_1","article-title":"Practical software project estimation; a toolkit for estimating software development efort & duration","volume":"65","author":"Fingerman S","year":"2011","unstructured":"S Fingerman. 2011. Practical software project estimation; a toolkit for estimating software development efort & duration. Sci-Tech News 65, 1 ( 2011 ), 28.","journal-title":"Sci-Tech News"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"crossref","unstructured":"Wei Fu Tim Menzies and Xipeng Shen. 2016. Tuning for software analytics: Is it really necessary? Information and Software Technology 76 ( 2016 ) 135-146.","DOI":"10.1016\/j.infsof.2016.04.017"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"crossref","unstructured":"Fernando Gonz\u00e1lez-Ladr\u00f3n-de Guevara Marta Fern\u00e1ndez-Diego and Chris Lokan. 2016. The usage of ISBSG data fields in software efort estimation: A systematic mapping study. Journal of Systems and Software 113 ( 2016 ) 188-215.","DOI":"10.1016\/j.jss.2015.11.040"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"crossref","unstructured":"Arthur E Hoerl and Robert W Kennard. 1970. Ridge regression: applications to nonorthogonal problems. Technometrics 12 1 ( 1970 ) 69-82.","DOI":"10.1080\/00401706.1970.10488635"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"crossref","unstructured":"Arthur E Hoerl and Robert W Kennard. 1970. Ridge regression: Biased estimation for nonorthogonal problems. Technometrics 12 1 ( 1970 ) 55-67.","DOI":"10.1080\/00401706.1970.10488634"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"crossref","unstructured":"Mohamed Hosni Ali Idri Alain Abran and Ali Bou Nassif. 2018. On the value of parameter tuning in heterogeneous ensembles efort estimation. Soft Computing 22 18 ( 2018 ) 5977-6010.","DOI":"10.1007\/s00500-017-2945-4"},{"key":"e_1_3_2_1_18_1","unstructured":"Chih-Wei Hsu Chih-Chung Chang and Chih-Jen Lin. 2003. A practical guide to support vector classification. https:\/\/www.csie.ntu.edu.tw\/~cjlin\/papers\/guide\/ guide.pdf. Accessed: 2020-07-07."},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2002.1031955"},{"key":"e_1_3_2_1_20_1","first-page":"16","volume-title":"MARP0. Information and Software Technology 73 ( 2016 )","author":"Langdon William B","unstructured":"William B Langdon, Javier Dolado, Federica Sarro, and Mark Harman. 2016. Exact mean absolute error of baseline predictor, MARP0. Information and Software Technology 73 ( 2016 ), 16-18."},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"crossref","unstructured":"Gang Luo. 2016. A review of automatic selection methods for machine learning algorithms and hyper-parameter values. Network Modeling Analysis in Health Informatics and Bioinformatics 5 1 ( 2016 ) 18.","DOI":"10.1007\/s13721-016-0125-6"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"crossref","unstructured":"Onkar Malgonde and Kaushal Chari. 2019. An ensemble-based model for predicting agile software development efort. Empirical Software Engineering 24 2 ( 2019 ) 1017-1055.","DOI":"10.1007\/s10664-018-9647-0"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"crossref","unstructured":"Leandro L Minku. 2019. A novel online supervised hyperparameter tuning procedure applied to cross-company software efort estimation. Empirical Software Engineering ( 2019 ) 1-52.","DOI":"10.1007\/s10664-019-09686-w"},{"key":"e_1_3_2_1_24_1","volume-title":"Ricardo MF Lima, and M\u00e1rcio L Corn\u00e9lio","author":"Oliveira Adriano LI","year":"2010","unstructured":"Adriano LI Oliveira, Petronio L Braga, Ricardo MF Lima, and M\u00e1rcio L Corn\u00e9lio. 2010. GA-based method for feature selection and parameters optimization for machine learning regression applied to software efort estimation. information and Software Technology 52, 11 ( 2010 ), 1155-1166."},{"key":"e_1_3_2_1_25_1","unstructured":"Robert Rosenthal Harris Cooper and L Hedges. 1994. Parametric measures of efect size. The handbook of research synthesis 621 2 ( 1994 ) 231-244."},{"key":"e_1_3_2_1_26_1","unstructured":"Bernhard Schlkopf Alexander J Smola and Francis Bach. 2018. Learning with kernels: support vector machines regularization optimization and beyond. the MIT Press."},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"crossref","unstructured":"Andrew Jhon Scott and M Knott. 1974. A cluster analysis method for grouping means in the analysis of variance. Biometrics ( 1974 ) 507-512.","DOI":"10.2307\/2529204"},{"key":"e_1_3_2_1_28_1","volume-title":"Understanding machine learning: From theory to algorithms","author":"Shalev-Shwartz Shai","unstructured":"Shai Shalev-Shwartz and Shai Ben-David. 2014. Understanding machine learning: From theory to algorithms. Cambridge university press."},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"crossref","unstructured":"Martin Shepperd and Steve MacDonell. 2012. Evaluating prediction systems in software project estimation. Information and Software Technology 54 8 ( 2012 ) 820-827.","DOI":"10.1016\/j.infsof.2011.12.008"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/2499393.2499394"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/2639490.2639510"},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/3295700"},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1145\/2884781.2884857"},{"key":"e_1_3_2_1_34_1","article-title":"The impact of automated parameter optimization on defect prediction models","volume":"45","author":"Tantithamthavorn Chakkrit","year":"2018","unstructured":"Chakkrit Tantithamthavorn, Shane McIntosh, Ahmed E Hassan, and Kenichi Matsumoto. 2018. The impact of automated parameter optimization on defect prediction models. IEEE Transactions on Software Engineering 45, 7 ( 2018 ), 683-711.","journal-title":"IEEE Transactions on Software Engineering"},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1145\/2487575.2487629"},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"crossref","unstructured":"Jianfeng Wen Shixian Li Zhiyong Lin Yong Hu and Changqin Huang. 2012. Systematic literature review of machine learning based software development efort estimation models. Information and Software Technology 54 1 ( 2012 ) 41-59.","DOI":"10.1016\/j.infsof.2011.09.002"},{"key":"e_1_3_2_1_37_1","volume-title":"Hyperparameter optimization for efort estimation. arXiv preprint arXiv","author":"Xia Tianpei","year":"1805","unstructured":"Tianpei Xia, Rahul Krishna, Jianfeng Chen, George Mathew, Xipeng Shen, and Tim Menzies. 2018. Hyperparameter optimization for efort estimation. arXiv preprint arXiv: 1805. 00336 ( 2018 )."},{"key":"e_1_3_2_1_38_1","unstructured":"Alice Zheng. 2015. Evaluating machine learning models: a beginner's guide to key concepts and pitfalls. ( 2015 )."}],"event":{"name":"PROMISE '20: 16th International Conference on Predictive Models and Data Analytics in Software Engineering","location":"Virtual USA","acronym":"PROMISE '20","sponsor":["SIGSOFT ACM Special Interest Group on Software Engineering"]},"container-title":["Proceedings of the 16th ACM International Conference on Predictive Models and Data Analytics in Software Engineering"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3416508.3417121","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3416508.3417121","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T22:41:30Z","timestamp":1750200090000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3416508.3417121"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,11,8]]},"references-count":38,"alternative-id":["10.1145\/3416508.3417121","10.1145\/3416508"],"URL":"https:\/\/doi.org\/10.1145\/3416508.3417121","relation":{},"subject":[],"published":{"date-parts":[[2020,11,8]]},"assertion":[{"value":"2020-11-08","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}