{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T21:26:21Z","timestamp":1771104381582,"version":"3.50.1"},"reference-count":145,"publisher":"Association for Computing Machinery (ACM)","issue":"5","license":[{"start":{"date-parts":[[2023,7,24]],"date-time":"2023-07-24T00:00:00Z","timestamp":1690156800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"NSF-CISE","award":["#1908762"],"award-info":[{"award-number":["#1908762"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Softw. Eng. Methodol."],"published-print":{"date-parts":[[2023,9,30]]},"abstract":"<jats:p>Before researchers rush to reason across all available data or try complex methods, perhaps it is prudent to first check for simpler alternatives. Specifically, if the historical data has the most information in some small region, then perhaps a model learned from that region would suffice for the rest of the project.<\/jats:p>\n          <jats:p>To support this claim, we offer a case study with 240 projects, where we find that the information in those projects \u201cclumps\u201d towards the earliest parts of the project. A quality prediction model learned from just the first 150 commits works as well, or better than state-of-the-art alternatives. Using just this \u201cearly bird\u201d data, we can build models very quickly and very early in the project life cycle. Moreover, using this early bird method, we have shown that a simple model (with just a few features) generalizes to hundreds of projects.<\/jats:p>\n          <jats:p>Based on this experience, we doubt that prior work on generalizing quality models may have needlessly complicated an inherently simple process. Further, prior work that focused on later-life cycle data needs to be revisited, since their conclusions were drawn from relatively uninformative regions.<\/jats:p>\n          <jats:p>Replication note: All our data and scripts are available here: https:\/\/github.com\/snaraya7\/early-bird.<\/jats:p>","DOI":"10.1145\/3583565","type":"journal-article","created":{"date-parts":[[2023,2,8]],"date-time":"2023-02-08T13:28:49Z","timestamp":1675862929000},"page":"1-39","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Assessing the Early Bird Heuristic (for Predicting Project Quality)"],"prefix":"10.1145","volume":"32","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8983-7733","authenticated-orcid":false,"given":"Shrikanth N.","family":"C.","sequence":"first","affiliation":[{"name":"North Carolina State University, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5040-3196","authenticated-orcid":false,"given":"Tim","family":"Menzies","sequence":"additional","affiliation":[{"name":"North Carolina State University, USA"}]}],"member":"320","published-online":{"date-parts":[[2023,7,24]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.5555\/645504.656414"},{"key":"e_1_3_2_3_2","article-title":"How to \u201cDODGE\u201d complex software analytics","author":"Agrawal Amritanshu","year":"2019","unstructured":"Amritanshu Agrawal, Wei Fu, Di Chen, Xipeng Shen, and Tim Menzies. 2019. How to \u201cDODGE\u201d complex software analytics. IEEE Trans. Softw. Eng. 47, 10 (2019), 2182\u20132194.","journal-title":"IEEE Trans. Softw. Eng."},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.infsof.2018.02.005"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1145\/3180155.3180197"},{"key":"e_1_3_2_6_2","article-title":"Simpler hyperparameter optimization for software analytics: Why, how, when","author":"Agrawal Amritanshu","year":"2021","unstructured":"Amritanshu Agrawal, Xueqi Yang, Rishabh Agrawal, Rahul Yedida, Xipeng Shen, and Tim Menzies. 2021. Simpler hyperparameter optimization for software analytics: Why, how, when. IEEE Trans. Softw. Eng. 48, 8 (2021), 2939\u20132954.","journal-title":"IEEE Trans. Softw. Eng."},{"key":"e_1_3_2_7_2","unstructured":"Fumio Akiyama. 1971. An example of software system debugging. In Information Processing Proceedings of IFIP Congress 1971 Volume 1 - Foundations and Systems Ljubljana Yugoslavia August 23-28 1971 Charles V. Freiman John E. Griffith and Jack L. Rosenfeld (Eds.). North-Holland 353\u2013359."},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10664-019-09777-8"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1145\/3377816.3381738"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10664-018-9633-6"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.5555\/2986459.2986743"},{"key":"e_1_3_2_12_2","first-page":"115","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Bergstra James","year":"2013","unstructured":"James Bergstra, Daniel Yamins, and David Cox. 2013. Making a science of model search: Hyperparameter optimization in hundreds of dimensions for vision architectures. In Proceedings of the International Conference on Machine Learning. PMLR, 115\u2013123."},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1109\/MSR.2012.6224300"},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.5555\/2886235"},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.1145\/2025113.2025119"},{"issue":"4","key":"e_1_3_2_16_2","first-page":"20","article-title":"COCOMO suite methodology and evolution","volume":"18","author":"Boehm Barry","year":"2005","unstructured":"Barry Boehm, Ricardo Valerdi, J. Lane, and A. W. Brown. 2005. COCOMO suite methodology and evolution. CrossTalk 18, 4 (2005), 20\u201325.","journal-title":"CrossTalk"},{"key":"e_1_3_2_17_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11219-016-9353-3"},{"key":"e_1_3_2_18_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.2002.1019484"},{"key":"e_1_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICST.2013.38"},{"key":"e_1_3_2_20_2","doi-asserted-by":"publisher","DOI":"10.1002\/stvr.1570"},{"key":"e_1_3_2_21_2","doi-asserted-by":"publisher","DOI":"10.1613\/jair.953"},{"key":"e_1_3_2_22_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10664-019-09778-7"},{"key":"e_1_3_2_23_2","doi-asserted-by":"crossref","unstructured":"Di Chen Wei Fu Rahul Krishna and Tim Menzies. 2018. Applications of psychological science for actionable analytics. In Proceedings of the 26th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering.","DOI":"10.1145\/3236024.3236050"},{"key":"e_1_3_2_24_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2890733"},{"key":"e_1_3_2_25_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.infsof.2015.01.014"},{"key":"e_1_3_2_26_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.infsof.2018.10.003"},{"key":"e_1_3_2_27_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.infsof.2017.08.004"},{"key":"e_1_3_2_28_2","article-title":"A deep tree-based model for software defect prediction","author":"Dam Hoa Khanh","year":"2018","unstructured":"Hoa Khanh Dam, Trang Pham, Shien Wee Ng, Truyen Tran, John Grundy, Aditya Ghose, Taeksu Kim, and Chul-Joo Kim. 2018. A deep tree-based model for software defect prediction. arXiv preprint arXiv:1802.00921 (2018).","journal-title":"arXiv preprint arXiv:1802.00921"},{"key":"e_1_3_2_29_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10664-011-9173-9"},{"key":"e_1_3_2_30_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10664-008-9072-x"},{"key":"e_1_3_2_31_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.infsof.2016.04.017"},{"key":"e_1_3_2_32_2","doi-asserted-by":"publisher","DOI":"10.1145\/2597073.2597075"},{"key":"e_1_3_2_33_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICSE.2015.91"},{"key":"e_1_3_2_34_2","doi-asserted-by":"publisher","DOI":"10.1147\/sj.411.0004"},{"key":"e_1_3_2_35_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2003.1245283"},{"key":"e_1_3_2_36_2","unstructured":"A. Hassan. 2017. Remarks made during a presentation to the UCL Crest Open Workshop. (March2017)."},{"key":"e_1_3_2_37_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2008.239"},{"key":"e_1_3_2_38_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.infsof.2014.11.006"},{"key":"e_1_3_2_39_2","doi-asserted-by":"publisher","DOI":"10.1109\/ESEM.2013.20"},{"key":"e_1_3_2_40_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10515-011-0090-3"},{"key":"e_1_3_2_41_2","doi-asserted-by":"publisher","DOI":"10.1145\/2499393.2499395"},{"key":"e_1_3_2_42_2","doi-asserted-by":"publisher","DOI":"10.1109\/MSR.2019.00016"},{"key":"e_1_3_2_43_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.infsof.2017.06.004"},{"key":"e_1_3_2_44_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10664-018-9661-2"},{"key":"e_1_3_2_45_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2817572"},{"key":"e_1_3_2_46_2","doi-asserted-by":"publisher","DOI":"10.1109\/ASE.2013.6693087"},{"key":"e_1_3_2_47_2","doi-asserted-by":"publisher","DOI":"10.1145\/2786805.2786813"},{"key":"e_1_3_2_48_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.2016.2597849"},{"key":"e_1_3_2_49_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10664-015-9400-x"},{"key":"e_1_3_2_50_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.2012.70"},{"key":"e_1_3_2_51_2","doi-asserted-by":"publisher","DOI":"10.1145\/1985793.1985815"},{"key":"e_1_3_2_52_2","doi-asserted-by":"publisher","DOI":"10.1145\/2884781.2884783"},{"key":"e_1_3_2_53_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10664-014-9300-5"},{"key":"e_1_3_2_54_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10664-018-9679-5"},{"key":"e_1_3_2_55_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10664-019-09736-3"},{"key":"e_1_3_2_56_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10664-008-9080-x"},{"key":"e_1_3_2_57_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.2008.90"},{"key":"e_1_3_2_58_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.2018.2821670"},{"key":"e_1_3_2_59_2","doi-asserted-by":"publisher","DOI":"10.1145\/2970276.2970339"},{"key":"e_1_3_2_60_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICPC.2017.24"},{"key":"e_1_3_2_61_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.infsof.2014.07.005"},{"key":"e_1_3_2_62_2","first-page":"777","volume-title":"Proceedings of the International Conference on Advances in Neural Information Processing Systems","author":"Levina Elizaveta","year":"2005","unstructured":"Elizaveta Levina and Peter J. Bickel. 2005. Maximum likelihood estimation of intrinsic dimension. In Proceedings of the International Conference on Advances in Neural Information Processing Systems. 777\u2013784."},{"key":"e_1_3_2_63_2","doi-asserted-by":"publisher","DOI":"10.1109\/QRS.2017.42"},{"key":"e_1_3_2_64_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10515-011-0092-1"},{"key":"e_1_3_2_65_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10515-017-0220-7"},{"key":"e_1_3_2_66_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.infsof.2018.11.005"},{"key":"e_1_3_2_67_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.infsof.2011.09.007"},{"key":"e_1_3_2_68_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.2018.2870388"},{"key":"e_1_3_2_69_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.2017.2693980"},{"key":"e_1_3_2_70_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.2012.83"},{"key":"e_1_3_2_71_2","doi-asserted-by":"publisher","DOI":"10.1109\/ASE.2011.6100072"},{"key":"e_1_3_2_72_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.2007.256941"},{"key":"e_1_3_2_73_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10515-010-0069-5"},{"key":"e_1_3_2_74_2","doi-asserted-by":"publisher","DOI":"10.1145\/1370788.1370801"},{"key":"e_1_3_2_75_2","doi-asserted-by":"publisher","DOI":"10.1609\/aimag.v32i2.2348"},{"key":"e_1_3_2_76_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.2012.45"},{"key":"e_1_3_2_77_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10664-017-9512-6"},{"key":"e_1_3_2_78_2","doi-asserted-by":"publisher","DOI":"10.1145\/1062455.1062514"},{"key":"e_1_3_2_79_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.2017.2720603"},{"key":"e_1_3_2_80_2","doi-asserted-by":"publisher","DOI":"10.5555\/2486788.2486839"},{"key":"e_1_3_2_81_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11390-017-1785-0"},{"key":"e_1_3_2_82_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.2005.49"},{"key":"e_1_3_2_83_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2010.2091281"},{"key":"e_1_3_2_84_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2019.113085"},{"key":"e_1_3_2_85_2","doi-asserted-by":"publisher","DOI":"10.1109\/CSMR-WCRE.2014.6747166"},{"key":"e_1_3_2_86_2","doi-asserted-by":"publisher","DOI":"10.1145\/2001420.2001445"},{"key":"e_1_3_2_87_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jss.2018.12.001"},{"key":"e_1_3_2_88_2","doi-asserted-by":"publisher","DOI":"10.1145\/2786984.2786995"},{"key":"e_1_3_2_89_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.2013.6"},{"key":"e_1_3_2_90_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICSE.2015.92"},{"key":"e_1_3_2_91_2","doi-asserted-by":"publisher","DOI":"10.5555\/2487085.2487161"},{"key":"e_1_3_2_92_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICSE.2013.6606589"},{"key":"e_1_3_2_93_2","doi-asserted-by":"publisher","DOI":"10.1145\/2568225.2568269"},{"key":"e_1_3_2_94_2","doi-asserted-by":"publisher","DOI":"10.1145\/2393596.2393669"},{"key":"e_1_3_2_95_2","doi-asserted-by":"publisher","DOI":"10.1145\/2491411.2491418"},{"key":"e_1_3_2_96_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2017.04.014"},{"issue":"4","key":"e_1_3_2_97_2","first-page":"419","article-title":"Software fault prediction based on change metrics using hybrid algorithms: An empirical study","volume":"32","author":"Rhmann Wasiur","year":"2020","unstructured":"Wasiur Rhmann, Babita Pandey, Gufran Ansari, and Devendra Kumar Pandey. 2020. Software fault prediction based on change metrics using hybrid algorithms: An empirical study. J. King Saud Univ.-Comp. Inf. Sci. 32, 4 (2020), 419\u2013424.","journal-title":"J. King Saud Univ.-Comp. Inf. Sci."},{"key":"e_1_3_2_98_2","doi-asserted-by":"publisher","DOI":"10.1145\/2786805.2803183"},{"key":"e_1_3_2_99_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2016.04.009"},{"key":"e_1_3_2_100_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10664-014-9346-4"},{"key":"e_1_3_2_101_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11390-015-1575-5"},{"key":"e_1_3_2_102_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11219-015-9287-1"},{"key":"e_1_3_2_103_2","doi-asserted-by":"publisher","DOI":"10.4018\/978-1-4666-2650-8.ch006"},{"key":"e_1_3_2_104_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.2014.2340398"},{"key":"e_1_3_2_105_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICSEE.2018.8645991"},{"key":"e_1_3_2_106_2","doi-asserted-by":"publisher","DOI":"10.1145\/1852786.1852792"},{"key":"e_1_3_2_107_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICSE43902.2021.00050"},{"key":"e_1_3_2_108_2","doi-asserted-by":"publisher","DOI":"10.1145\/3377813.3381367"},{"key":"e_1_3_2_109_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.is.2015.02.006"},{"key":"e_1_3_2_110_2","doi-asserted-by":"publisher","DOI":"10.1145\/1082983.1083147"},{"key":"e_1_3_2_111_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.2018.2836442"},{"key":"e_1_3_2_112_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSMCC.2012.2226152"},{"key":"e_1_3_2_113_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICSE.2015.139"},{"key":"e_1_3_2_114_2","article-title":"Defining and conceptualizing actionable insight: A conceptual framework for decision-centric analytics","author":"Tan Shiang-Yen","year":"2016","unstructured":"Shiang-Yen Tan and Taizan Chan. 2016. Defining and conceptualizing actionable insight: A conceptual framework for decision-centric analytics. arXiv preprint arXiv:1606.03510 (2016).","journal-title":"arXiv preprint arXiv:1606.03510"},{"key":"e_1_3_2_115_2","article-title":"The impact of class rebalancing techniques on the performance and interpretation of defect prediction models","author":"Tantithamthavorn C.","year":"2018","unstructured":"C. Tantithamthavorn, A. E. Hassan, and K. Matsumoto. 2018. The impact of class rebalancing techniques on the performance and interpretation of defect prediction models. IEEE Trans. Softw. Eng. 46, 11 (2018), 1200\u20131219.","journal-title":"IEEE Trans. Softw. Eng."},{"key":"e_1_3_2_116_2","doi-asserted-by":"publisher","DOI":"10.1145\/2884781.2884857"},{"key":"e_1_3_2_117_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.2018.2794977"},{"key":"e_1_3_2_118_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.infsof.2017.11.008"},{"key":"e_1_3_2_119_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.infsof.2012.10.003"},{"issue":"2","key":"e_1_3_2_120_2","first-page":"101","article-title":"A critique and improvement of the CL common language effect size statistics of McGraw and Wong","volume":"25","author":"Vargha Andr\u00e1s","year":"2000","unstructured":"Andr\u00e1s Vargha and Harold D. Delaney. 2000. A critique and improvement of the CL common language effect size statistics of McGraw and Wong. J. Educ. Behav. Statist. 25, 2 (2000), 101\u2013132.","journal-title":"J. Educ. Behav. Statist."},{"key":"e_1_3_2_121_2","article-title":"Perceptions, expectations, and challenges in defect prediction","author":"Wan Zhiyuan","year":"2018","unstructured":"Zhiyuan Wan, Xin Xia, Ahmed E. Hassan, David Lo, Jianwei Yin, and Xiaohu Yang. 2018. Perceptions, expectations, and challenges in defect prediction. IEEE Trans. Softw. Eng. 46, 11 (2018), 1241\u20131266.","journal-title":"IEEE Trans. Softw. Eng."},{"key":"e_1_3_2_122_2","article-title":"Deep semantic feature learning for software defect prediction","author":"Wang Song","year":"2018","unstructured":"Song Wang, Taiyue Liu, Jaechang Nam, and Lin Tan. 2018. Deep semantic feature learning for software defect prediction. IEEE Trans. Softw. Eng. 46, 12 (2018), 1267\u20131293.","journal-title":"IEEE Trans. Softw. Eng."},{"key":"e_1_3_2_123_2","doi-asserted-by":"publisher","DOI":"10.1145\/2884781.2884804"},{"key":"e_1_3_2_124_2","doi-asserted-by":"publisher","DOI":"10.1109\/TR.2013.2259203"},{"key":"e_1_3_2_125_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10515-015-0179-1"},{"key":"e_1_3_2_126_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10664-008-9082-8"},{"key":"e_1_3_2_127_2","doi-asserted-by":"publisher","DOI":"10.1109\/TR.2018.2804922"},{"key":"e_1_3_2_128_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.2016.2543218"},{"key":"e_1_3_2_129_2","doi-asserted-by":"publisher","DOI":"10.1145\/2961111.2962606"},{"key":"e_1_3_2_130_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.infsof.2018.10.004"},{"key":"e_1_3_2_131_2","article-title":"Just-in-time defect identification and localization: A two-phase framework","author":"Yan Meng","year":"2020","unstructured":"Meng Yan, Xin Xia, Yuanrui Fan, Ahmed E. Hassan, David Lo, and Shanping Li. 2020. Just-in-time defect identification and localization: A two-phase framework. IEEE Trans. Softw. Eng. 48, 1 (2020), 82\u2013101.","journal-title":"IEEE Trans. Softw. Eng."},{"key":"e_1_3_2_132_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10664-019-09688-8"},{"key":"e_1_3_2_133_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.infsof.2017.03.007"},{"key":"e_1_3_2_134_2","doi-asserted-by":"publisher","DOI":"10.1145\/2950290.2950353"},{"key":"e_1_3_2_135_2","doi-asserted-by":"publisher","DOI":"10.1145\/3383219.3383232"},{"key":"e_1_3_2_136_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICSE.2019.00075"},{"key":"e_1_3_2_137_2","doi-asserted-by":"publisher","DOI":"10.1145\/2635868.2635874"},{"key":"e_1_3_2_138_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.2016.2599161"},{"key":"e_1_3_2_139_2","doi-asserted-by":"publisher","DOI":"10.1145\/2597073.2597078"},{"key":"e_1_3_2_140_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10664-015-9396-2"},{"key":"e_1_3_2_141_2","doi-asserted-by":"publisher","DOI":"10.1145\/2884781.2884839"},{"key":"e_1_3_2_142_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICSM.2010.5609671"},{"key":"e_1_3_2_143_2","doi-asserted-by":"publisher","DOI":"10.1109\/COMPSAC.2015.58"},{"key":"e_1_3_2_144_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.infsof.2019.07.003"},{"key":"e_1_3_2_145_2","doi-asserted-by":"publisher","DOI":"10.1145\/3183339"},{"key":"e_1_3_2_146_2","doi-asserted-by":"publisher","DOI":"10.1145\/1595696.1595713"}],"container-title":["ACM Transactions on Software Engineering and Methodology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3583565","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3583565","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:37:54Z","timestamp":1750178274000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3583565"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,24]]},"references-count":145,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2023,9,30]]}},"alternative-id":["10.1145\/3583565"],"URL":"https:\/\/doi.org\/10.1145\/3583565","relation":{},"ISSN":["1049-331X","1557-7392"],"issn-type":[{"value":"1049-331X","type":"print"},{"value":"1557-7392","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,24]]},"assertion":[{"value":"2022-07-22","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-01-10","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-07-24","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}