{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,6]],"date-time":"2026-07-06T15:36:08Z","timestamp":1783352168700,"version":"3.54.6"},"reference-count":78,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2022,6,22]],"date-time":"2022-06-22T00:00:00Z","timestamp":1655856000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,6,22]],"date-time":"2022-06-22T00:00:00Z","timestamp":1655856000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100008982","name":"National Science Foundation","doi-asserted-by":"publisher","award":["1703487"],"award-info":[{"award-number":["1703487"]}],"id":[{"id":"10.13039\/501100008982","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Empir Software Eng"],"published-print":{"date-parts":[[2022,11]]},"DOI":"10.1007\/s10664-022-10171-0","type":"journal-article","created":{"date-parts":[[2022,6,22]],"date-time":"2022-06-22T08:14:49Z","timestamp":1655885689000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Predicting health indicators for open source projects (using hyperparameter optimization)"],"prefix":"10.1007","volume":"27","author":[{"given":"Tianpei","family":"Xia","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Fu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rui","family":"Shu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rishabh","family":"Agrawal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5040-3196","authenticated-orcid":false,"given":"Tim","family":"Menzies","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,6,22]]},"reference":[{"key":"10171_CR1","doi-asserted-by":"crossref","unstructured":"Aggarwal K, Hindle A, Stroulia E (2014) Co-evolution of project documentation and popularity within github. In: Proceedings of the 11th working conference on mining software repositories, pp 360\u2013363","DOI":"10.1145\/2597073.2597120"},{"key":"10171_CR2","unstructured":"Agrawal A, Fu W, Chen D, Shen X, Menzies T (2019) How to\u201d DODGE\u201d complex software analytics. IEEE Trans Softw Eng"},{"key":"10171_CR3","doi-asserted-by":"crossref","unstructured":"Agrawal A, Menzies T (2018) Is\u201d better data\u201d better than\u201d better data miners\u201d?. In: 2018 IEEE\/ACM 40th international conference on software engineering (ICSE), IEEE, pp 1050\u20131061","DOI":"10.1145\/3180155.3180197"},{"key":"10171_CR4","unstructured":"Agrawal A, Menzies T, Minku LL, Wagner M, Yu Z (2018) Better software analytics via\u201d DUO\u201d: Data mining algorithms using\/used-by optimizers. arXiv:1812.01550"},{"key":"10171_CR5","doi-asserted-by":"publisher","unstructured":"Agrawal A, Yang X, Agrawal R, Yedida R, Shen X, Menzies T (2021) Simpler hyperparameter optimization for software analytics: Why, how, when. IEEE Trans Softw Eng, 1\u20131. https:\/\/doi.org\/10.1109\/TSE.2021.3073242","DOI":"10.1109\/TSE.2021.3073242"},{"key":"10171_CR6","unstructured":"Bao L, Xia X, Lo D, Murphy GC (2019) A large scale study of long-time contributor prediction for github projects. IEEE Trans Softw Eng"},{"key":"10171_CR7","unstructured":"Bergstra JS, Bardenet R, Bengio Y, K\u00e9gl B (2011) Algorithms for hyper-parameter optimization. In: Advances in neural information processing systems, pp 2546\u20132554"},{"key":"10171_CR8","doi-asserted-by":"crossref","unstructured":"Bidoki NH, Sukthankar G, Keathley H, Garibay I (2018) A cross-repository model for predicting popularity in github. In: 2018 international conference on computational science and computational intelligence (CSCI), IEEE, pp 1248\u20131253","DOI":"10.1109\/CSCI46756.2018.00241"},{"key":"10171_CR9","doi-asserted-by":"crossref","unstructured":"Borges H, Hora A, Valente MT (2016a) Predicting the popularity of github repositories. In: Proceedings of the The 12th international conference on predictive models and data analytics in software engineering, pp 1\u201310","DOI":"10.1145\/2972958.2972966"},{"key":"10171_CR10","doi-asserted-by":"crossref","unstructured":"Borges H, Hora A, Valente MT (2016b) Understanding the factors that impact the popularity of github repositories. In: 2016 IEEE international conference on software maintenance and evolution (ICSME), IEEE, pp 334\u2013344","DOI":"10.1109\/ICSME.2016.31"},{"issue":"8","key":"10171_CR11","first-page":"820","volume":"54","author":"M C","year":"2012","unstructured":"C M, MacDonell S (2012) Evaluating prediction systems in software project estimation. IST 54(8):820\u2013827","journal-title":"IST"},{"key":"10171_CR12","first-page":"3","volume":"12","author":"C Chen","year":"2017","unstructured":"Chen C, Twycross J, Garibaldi JM (2017) A new accuracy measure based on bounded relative error for time series forecasting. PloS One 12:3","journal-title":"PloS One"},{"key":"10171_CR13","doi-asserted-by":"crossref","unstructured":"Chen F, Li L, Jiang J, Zhang L (2014) Predicting the number of forks for open source software project. In: Proceedings of the 2014 3rd International workshop on evidential assessment of software technologies, pp 40\u201347","DOI":"10.1145\/2627508.2627515"},{"key":"10171_CR14","doi-asserted-by":"crossref","unstructured":"Coelho J, Valente M T, Milen L, Silva L L (2020) Is this github project maintained? measuring the level of maintenance activity of open-source projects. Information and Software Technology 122","DOI":"10.1016\/j.infsof.2020.106274"},{"key":"10171_CR15","volume-title":"Empirical methods for artificial intelligence","author":"PR Cohen","year":"1995","unstructured":"Cohen PR (1995) Empirical methods for artificial intelligence. MIT Press, Cambridge, MA, USA"},{"issue":"5","key":"10171_CR16","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1109\/MC.2006.152","volume":"39","author":"K Crowston","year":"2006","unstructured":"Crowston K, Howison J (2006) Assessing the health of open source communities. Computer 39(5):89\u201391","journal-title":"Computer"},{"key":"10171_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.swevo.2016.01.004","volume":"27","author":"S Das","year":"2016","unstructured":"Das S, Mullick S S, Suganthan P N (2016) Recent advances in differential evolution\u2013an updated survey. Swarm and Evolutionary Computation 27:1\u201330","journal-title":"Swarm and Evolutionary Computation"},{"key":"10171_CR18","first-page":"1","volume":"7","author":"J Dem\u0161ar","year":"2006","unstructured":"Dem\u0161ar J (2006) Statistical comparisons of classifiers over multiple data sets. The Journal of Machine Learning Research 7:1\u201330","journal-title":"The Journal of Machine Learning Research"},{"key":"10171_CR19","unstructured":"Feldt R, Magazinius A (2010) Validity threats in empirical software engineering research-an initial survey. In: SEKE, pp 374\u2013379"},{"key":"10171_CR20","doi-asserted-by":"crossref","unstructured":"Feurer M, Klein A, Eggensperger K, Springenberg J T, Blum M, Hutter F (2019) Auto-sklearn: Efficient and robust automated machine learning. In: Automated Machine Learning. Springer, Cham, pp 113\u2013134","DOI":"10.1007\/978-3-030-05318-5_6"},{"issue":"11","key":"10171_CR21","first-page":"985","volume":"29","author":"T Foss","year":"2003","unstructured":"Foss T, Stensrud E, Kitchenham B, Myrtveit I (2003) A simulation study of the model evaluation criterion mmre. TSE 29(11):985\u2013995","journal-title":"TSE"},{"key":"10171_CR22","unstructured":"Foundation A S (2018) Apache software foundation projects https:\/\/projects.apache.org\/projects.html"},{"key":"10171_CR23","unstructured":"Foundation L (2020) Community health analytics open source software https:\/\/chaoss.community\/"},{"key":"10171_CR24","unstructured":"Foundation L (2020) Linux foundation projects https:\/\/www.linuxfoundation.org\/projects\/directory\/"},{"issue":"1","key":"10171_CR25","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1214\/aoms\/1177731944","volume":"11","author":"M Friedman","year":"1940","unstructured":"Friedman M (1940) A comparison of alternative tests of significance for the problem of m rankings. The Annals of Mathematical Statistics 11(1):86\u201392","journal-title":"The Annals of Mathematical Statistics"},{"key":"10171_CR26","first-page":"135","volume":"76","author":"W Fu","year":"2016","unstructured":"Fu W, Menzies T, Shen X (2016) Tuning for software analytics: Is it really necessary?. IST Journal 76:135\u2013146","journal-title":"IST Journal"},{"key":"10171_CR27","unstructured":"Fu W, Nair V, Menzies T (2016) Why is differential evolution better than grid search for tuning defect predictors?. arXiv:1609.02613"},{"key":"10171_CR28","unstructured":"Georg JPL, Germonprez M (2018) Assessing open source project health"},{"key":"10171_CR29","doi-asserted-by":"crossref","unstructured":"Han J, Deng S, Xia X, Wang D, Yin J (2019) Characterization and prediction of popular projects on github. In: 2019 IEEE 43rd annual computer software and applications conference (COMPSAC), IEEE, vol 1, pp 21\u201326","DOI":"10.1109\/COMPSAC.2019.00013"},{"issue":"11","key":"10171_CR30","doi-asserted-by":"publisher","first-page":"1091","DOI":"10.1109\/TSE.2017.2748129","volume":"43","author":"S Herbold","year":"2017","unstructured":"Herbold S (2017) Comments on scottknottesd in response to\u201d an empirical comparison of model validation techniques for defect prediction models\u201d. IEEE Trans Softw Eng 43(11):1091\u20131094","journal-title":"IEEE Trans Softw Eng"},{"issue":"6","key":"10171_CR31","doi-asserted-by":"publisher","first-page":"632","DOI":"10.1109\/TSE.2018.2790413","volume":"45","author":"S Herbold","year":"2018","unstructured":"Herbold S, Trautsch A, Grabowski J (2018) Correction of \u201cA comparative study to benchmark cross-project defect prediction approaches\u201d. IEEE Trans Softw Eng 45(6):632\u2013636","journal-title":"IEEE Trans Softw Eng"},{"key":"10171_CR32","doi-asserted-by":"crossref","unstructured":"Hohl P, Stupperich M, M\u00fcnch J, Schneider K (2018) An assessment model to foster the adoption of agile software product lines in the automotive domain. In: 2018 IEEE international conference on engineering, technology and innovation (ICE\/ITMC), IEEE, pp 1\u20139","DOI":"10.1109\/ICE.2018.8436325"},{"issue":"11","key":"10171_CR33","doi-asserted-by":"publisher","first-page":"1508","DOI":"10.1016\/j.infsof.2014.04.006","volume":"56","author":"S Jansen","year":"2014","unstructured":"Jansen S (2014) Measuring the health of open source software ecosystems: Beyond the scope of project health. Inf Softw Technol 56(11):1508\u20131519","journal-title":"Inf Softw Technol"},{"key":"10171_CR34","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1016\/j.infsof.2018.03.010","volume":"100","author":"O Jarczyk","year":"2018","unstructured":"Jarczyk O, Jaroszewicz S, Wierzbicki A, Pawlak K, Jankowski-Lorek M (2018) Surgical teams on github: Modeling performance of github project development processes. Inf Softw Technol 100:32\u201346","journal-title":"Inf Softw Technol"},{"key":"10171_CR35","doi-asserted-by":"crossref","unstructured":"Kalliamvakou E, Gousios G, Blincoe K, Singer L, German D M, Damian D (2014) The promises and perils of mining github. In: Proceedings of the 11th working conference on mining software repositories, pp 92\u2013101","DOI":"10.1145\/2597073.2597074"},{"issue":"5","key":"10171_CR36","doi-asserted-by":"publisher","first-page":"2035","DOI":"10.1007\/s10664-015-9393-5","volume":"21","author":"E Kalliamvakou","year":"2016","unstructured":"Kalliamvakou E, Gousios G, Blincoe K, Singer L, German D M, Damian D (2016) An in-depth study of the promises and perils of mining github. Empir Softw Eng 21(5):2035\u20132071","journal-title":"Empir Softw Eng"},{"key":"10171_CR37","doi-asserted-by":"crossref","unstructured":"Kikas R, Dumas M, Pfahl D (2016) Using dynamic and contextual features to predict issue lifetime in github projects. In: 2016 IEEE\/ACM 13th working conference on mining software repositories (MSR), IEEE, pp 291\u2013302","DOI":"10.1145\/2901739.2901751"},{"issue":"3","key":"10171_CR38","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1049\/ip-sen:20010506","volume":"148","author":"BA Kitchenham","year":"2001","unstructured":"Kitchenham B A, Pickard L M, MacDonell S G, Shepperd M J (2001) What accuracy statistics really measure. IEEE Softw 148(3):81\u201385","journal-title":"IEEE Softw"},{"key":"10171_CR39","doi-asserted-by":"crossref","unstructured":"Korte M, Port D (2008) Confidence in software cost estimation results based on mmre and pred. In: PROMISE\u201908, pp 63\u201370","DOI":"10.1145\/1370788.1370804"},{"key":"10171_CR40","doi-asserted-by":"crossref","unstructured":"Krishna R, Agrawal A, Rahman A, Sobran A, Menzies T (2018) What is the connection between issues, bugs, and enhancements?. In: 2018 IEEE\/ACM 40th international conference on software engineering: software engineering in practice track (ICSE-SEIP), IEEE, pp 306\u2013315","DOI":"10.1145\/3183519.3183548"},{"issue":"12","key":"10171_CR41","doi-asserted-by":"publisher","first-page":"2956","DOI":"10.1109\/TSE.2020.2983927","volume":"47","author":"R Krishna","year":"2021","unstructured":"Krishna R, Nair V, Jamshidi P, Menzies T (2021) Whence to learn? transferring knowledge in configurable systems using BEETLE. IEEE Trans Softw Eng 47(12):2956\u20132972. https:\/\/doi.org\/10.1109\/TSE.2020.2983927","journal-title":"IEEE Trans Softw Eng"},{"key":"10171_CR42","first-page":"16","volume":"73","author":"WB Langdon","year":"2016","unstructured":"Langdon W B, Dolado J, Sarro F, Harman M (2016) Exact mean absolute error of baseline predictor, MARP0. IST 73:16\u201318","journal-title":"IST"},{"issue":"2","key":"10171_CR43","doi-asserted-by":"publisher","first-page":"144","DOI":"10.3390\/sym11020144","volume":"11","author":"Z Liao","year":"2019","unstructured":"Liao Z, Yi M, Wang Y, Liu S, Liu H, Zhang Y, Zhou Y (2019) Healthy or not: A way to predict ecosystem health in github. Symmetry 11(2):144","journal-title":"Symmetry"},{"key":"10171_CR44","unstructured":"Manikas K, Hansen K M (2013) Reviewing the health of software ecosystems-a conceptual framework proposal. In: Proceedings of the 5th international workshop on software ecosystems (IWSECO), Citeseer, pp 33\u201344"},{"issue":"5","key":"10171_CR45","doi-asserted-by":"publisher","first-page":"3153","DOI":"10.1007\/s10664-019-09686-w","volume":"24","author":"LL Minku","year":"2019","unstructured":"Minku L L (2019) A novel online supervised hyperparameter tuning procedure applied to cross-company software effort estimation. Empir Softw Eng 24 (5):3153\u20133204","journal-title":"Empir Softw Eng"},{"key":"10171_CR46","doi-asserted-by":"crossref","unstructured":"Molokken K, Jorgensen M (2003) A review of software surveys on software effort estimation. In: Empirical Software Engineering, 2003. ISESE 2003. Proceedings. 2003 International Symposium on, IEEE, pp 223\u2013230","DOI":"10.1109\/ISESE.2003.1237981"},{"key":"10171_CR47","doi-asserted-by":"crossref","unstructured":"Molokken K, Jorgensen M (2003) A review of software surveys on software effort estimation. In: 2003 International Symposium on Empirical Software Engineering, 2003. ISESE 2003. Proceedings, IEEE, pp 223\u2013230","DOI":"10.1109\/ISESE.2003.1237981"},{"issue":"6","key":"10171_CR48","doi-asserted-by":"publisher","first-page":"3219","DOI":"10.1007\/s10664-017-9512-6","volume":"22","author":"N Munaiah","year":"2017","unstructured":"Munaiah N, Kroh S, Cabrey C, Nagappan M (2017) Curating github for engineered software projects. Empir Softw Eng 22(6):3219\u20133253","journal-title":"Empir Softw Eng"},{"key":"10171_CR49","doi-asserted-by":"crossref","unstructured":"Nagy A, Njima M, Mkrtchyan L (2010) A bayesian based method for agile software development release planning and project health monitoring. In: 2010 international conference on intelligent networking and collaborative systems, IEEE, pp 192\u2013199","DOI":"10.1109\/INCOS.2010.99"},{"key":"10171_CR50","doi-asserted-by":"publisher","unstructured":"Nair V, Yu Z, Menzies T, Siegmund N, Apel S (2018) Finding faster configurations using flash. IEEE Transactions on Software Engineering 1\u20131. https:\/\/doi.org\/10.1109\/TSE.2018.2870895","DOI":"10.1109\/TSE.2018.2870895"},{"key":"10171_CR51","unstructured":"Nemenyi PB (1963) Distribution-free multiple comparisons. Princeton University"},{"issue":"5","key":"10171_CR52","doi-asserted-by":"publisher","first-page":"2550","DOI":"10.1007\/s10664-017-9555-8","volume":"23","author":"M Paasivaara","year":"2018","unstructured":"Paasivaara M, Behm B, Lassenius C, Hallikainen M (2018) Large-scale agile transformation at ericsson: a case study. Empir Softw Eng 23(5):2550\u20132596","journal-title":"Empir Softw Eng"},{"issue":"3","key":"10171_CR53","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1109\/MS.2017.86","volume":"34","author":"C Parnin","year":"2017","unstructured":"Parnin C, Helms E, Atlee C, Boughton H, Ghattas M, Glover A, Holman J, Micco J, Murphy B, Savor T et al (2017) The top 10 adages in continuous deployment. IEEE Softw 34(3):86\u201395","journal-title":"IEEE Softw"},{"key":"10171_CR54","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V et al (2011) Scikit-learn: Machine learning in python. J Mach Learn Res 12:2825\u20132830","journal-title":"J Mach Learn Res"},{"key":"10171_CR55","doi-asserted-by":"crossref","unstructured":"Port D, Korte M (2008) Comparative studies of the model evaluation criterion mmre and pred in software cost estimation research. In: ESEM\u201908, pp 51\u201360","DOI":"10.1145\/1414004.1414015"},{"key":"10171_CR56","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1016\/j.infsof.2017.07.015","volume":"92","author":"F Qi","year":"2017","unstructured":"Qi F, Jing X-Y, Zhu X, Xie X, Xu B, Ying S (2017) Software effort estimation based on open source projects: Case study of github. Inf Softw Technol 92:145\u2013157","journal-title":"Inf Softw Technol"},{"key":"10171_CR57","doi-asserted-by":"crossref","unstructured":"Santos A R, Kroll J, Sales A, Fernandes P, Wildt D (2016) Investigating the adoption of agile practices in mobile application development. In: ICEIS (1), pp 490\u2013497","DOI":"10.5220\/0005835404900497"},{"key":"10171_CR58","doi-asserted-by":"crossref","unstructured":"Sarro F, Petrozziello A, Harman M (2016) Multi-objective software effort estimation. In: ICSE, ACM, pp 619\u2013630","DOI":"10.1145\/2884781.2884830"},{"issue":"3","key":"10171_CR59","first-page":"175","volume":"5","author":"M Shepperd","year":"2000","unstructured":"Shepperd M, Cartwright M, Kadoda G (2000) On building prediction systems for software engineers. EMSE 5(3):175\u2013182","journal-title":"EMSE"},{"key":"10171_CR60","unstructured":"Shrikanth NC, Menzies T (2021) The early bird catches the worm: Better early life cycle defect predictors. arXiv:2105.11082"},{"key":"10171_CR61","unstructured":"Snoek J, Larochelle H, Adams R P (2012) Practical bayesian optimization of machine learning algorithms. arXiv:1206.2944"},{"issue":"2","key":"10171_CR62","first-page":"139","volume":"8","author":"E Stensrud","year":"2003","unstructured":"Stensrud E, Foss T, Kitchenham B, Myrtveit I (2003) A further empirical investigation of the relationship of mre and project size. ESE 8(2):139\u2013161","journal-title":"ESE"},{"key":"10171_CR63","unstructured":"Stewart K (2019) Personnel communication"},{"issue":"4","key":"10171_CR64","first-page":"341","volume":"11","author":"R Storn","year":"1997","unstructured":"Storn R, Price K (1997) Differential evolution\u2013a simple and efficient heuristic for global optimization over cont. spaces. JoGO 11(4):341\u2013359","journal-title":"JoGO"},{"key":"10171_CR65","doi-asserted-by":"crossref","unstructured":"Tantithamthavorn C, McIntosh S, Hassan A E, Matsumoto K (2016) Automated parameter optimization of classification techniques for defect prediction models. In: Proceedings of the 38th international conference on software engineering, pp 321\u2013332","DOI":"10.1145\/2884781.2884857"},{"issue":"7","key":"10171_CR66","doi-asserted-by":"publisher","first-page":"683","DOI":"10.1109\/TSE.2018.2794977","volume":"45","author":"C Tantithamthavorn","year":"2018","unstructured":"Tantithamthavorn C, McIntosh S, Hassan A E, Matsumoto K (2018) The impact of automated parameter optimization on defect prediction models. IEEE Trans Softw Eng 45(7):683\u2013711","journal-title":"IEEE Trans Softw Eng"},{"key":"10171_CR67","unstructured":"Tu H, Menzies T (2021) Frugal: Unlocking ssl for software analytics"},{"key":"10171_CR68","doi-asserted-by":"crossref","unstructured":"Tu H, Papadimitriou G, Kiran M, Wang C, Mandal A, Deelman E, Menzies T (2021) Mining workflows for anomalous data transfers. In: 2021 IEEE\/ACM 18th international conference on mining software repositories (MSR), pp 1\u201312","DOI":"10.1109\/MSR52588.2021.00013"},{"key":"10171_CR69","doi-asserted-by":"crossref","unstructured":"Wahyudin D, Mustofa K, Schatten A, Biffl S, Tjoa A M (2007) Monitoring the \u201chealth\u201d status of open source web-engineering projects. International Journal of Web Information Systems","DOI":"10.1108\/17440080710829252"},{"key":"10171_CR70","doi-asserted-by":"crossref","unstructured":"Wang T, Zhang Y, Yin G, Yu Y, Wang H (2018) Who will become a long-term contributor? a prediction model based on the early phase behaviors. In: Proceedings of the Tenth Asia-Pacific symposium on internetware, pp 1\u201310","DOI":"10.1145\/3275219.3275223"},{"key":"10171_CR71","doi-asserted-by":"crossref","unstructured":"Weber S, Luo J (2014) What makes an open source code popular on git hub?. In: 2014 IEEE international conference on data mining workshop, IEEE, pp 851\u2013855","DOI":"10.1109\/ICDMW.2014.55"},{"key":"10171_CR72","volume-title":"Data mining: Practical machine learning tools and techniques","author":"IH Witten","year":"2011","unstructured":"Witten I H, Frank E, Hall M A (2011) Data mining: Practical machine learning tools and techniques, 3rd edn. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA","edition":"3rd edn."},{"key":"10171_CR73","doi-asserted-by":"publisher","first-page":"172","DOI":"10.1016\/j.ins.2017.09.053","volume":"423","author":"G Wu","year":"2018","unstructured":"Wu G, Shen X, Li H, Chen H, Lin A, Suganthan P N (2018) Ensemble of differential evolution variants. Inf Sci 423:172\u2013186","journal-title":"Inf Sci"},{"key":"10171_CR74","doi-asserted-by":"crossref","unstructured":"Wynn Jr D (2007) Assessing the health of an open source ecosystem. In: Emerging Free and Open Source Software Practices. IGI Global, pp 238\u2013258","DOI":"10.4018\/978-1-59904-210-7.ch011"},{"key":"10171_CR75","unstructured":"Xia T (2021) Principles of project health for open source software"},{"key":"10171_CR76","unstructured":"Xia T, Shu R, Shen X, Menzies T (2020) Sequential model optimization for software effort estimation. IEEE Transactions on Software Engineering"},{"key":"10171_CR77","doi-asserted-by":"publisher","first-page":"204","DOI":"10.1016\/j.infsof.2016.01.004","volume":"74","author":"Y Yu","year":"2016","unstructured":"Yu Y, Wang H, Yin G, Wang T (2016) Reviewer recommendation for pull-requests in github: What can we learn from code review and bug assignment?. Inf Softw Technol 74:204\u2013218","journal-title":"Inf Softw Technol"},{"key":"10171_CR78","unstructured":"Zemlin J (2017) If you can\u2019t measure it, you can\u2019t improve it. https:\/\/www.linux.com\/news\/if-you-cant-measure-it-you-cant-improve-it-chaoss-project-creates-tools-analyze-software\/"}],"container-title":["Empirical Software Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10664-022-10171-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10664-022-10171-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10664-022-10171-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,26]],"date-time":"2022-09-26T08:13:06Z","timestamp":1664179986000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10664-022-10171-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,22]]},"references-count":78,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2022,11]]}},"alternative-id":["10171"],"URL":"https:\/\/doi.org\/10.1007\/s10664-022-10171-0","relation":{},"ISSN":["1382-3256","1573-7616"],"issn-type":[{"value":"1382-3256","type":"print"},{"value":"1573-7616","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,22]]},"assertion":[{"value":"17 March 2022","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 June 2022","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"122"}}