{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T06:00:32Z","timestamp":1780898432343,"version":"3.54.1"},"publisher-location":"Cham","reference-count":43,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032248381","type":"print"},{"value":"9783032248398","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-3-032-24839-8_5","type":"book-chapter","created":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T06:46:07Z","timestamp":1777445167000},"page":"66-81","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Search-based Hyperparameter Tuning for Python Unit Test Generation"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0092-3476","authenticated-orcid":false,"given":"Stephan","family":"Lukasczyk","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4364-6595","authenticated-orcid":false,"given":"Gordon","family":"Fraser","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,4,30]]},"reference":[{"key":"5_CR1","doi-asserted-by":"publisher","unstructured":"Almulla, H.K., Gay, G.: Learning how to search: generating effective test cases through adaptive fitness function selection. Empir. Softw. Eng. 27(2), 38 (2022). https:\/\/doi.org\/10.1007\/s10664-021-10048-8","DOI":"10.1007\/s10664-021-10048-8"},{"key":"5_CR2","doi-asserted-by":"publisher","unstructured":"Arcuri, A.: Test suite generation with the many independent objective (MIO) algorithm. Inf. Softw. Technol. 104, 195\u2013206 (2018). https:\/\/doi.org\/10.1016\/j.infsof.2018.05.003","DOI":"10.1016\/j.infsof.2018.05.003"},{"issue":"3","key":"5_CR3","doi-asserted-by":"publisher","first-page":"219","DOI":"10.1002\/stvr.1486","volume":"24","author":"A Arcuri","year":"2014","unstructured":"Arcuri, A., Briand, L.C.: A hitchhiker\u2019s guide to statistical tests for assessing randomized algorithms in software engineering. Softw. Test. Verification Reliab. 24(3), 219\u2013250 (2014). https:\/\/doi.org\/10.1002\/stvr.1486","journal-title":"Softw. Test. Verification Reliab."},{"key":"5_CR4","doi-asserted-by":"publisher","unstructured":"Arcuri, A., Fraser, G.: On parameter tuning in search based software engineering. In: Proceedings of the SSBSE 2011. LNCS, vol. 6956, pp. 33\u201347. Springer, Heidelberg (2011). https:\/\/doi.org\/10.1007\/978-3-642-23716-4_6","DOI":"10.1007\/978-3-642-23716-4_6"},{"issue":"3","key":"5_CR5","doi-asserted-by":"publisher","first-page":"594","DOI":"10.1007\/s10664-013-9249-9","volume":"18","author":"A Arcuri","year":"2013","unstructured":"Arcuri, A., Fraser, G.: Parameter tuning or default values? An empirical investigation in search-based software engineering. Empir. Softw. Eng. 18(3), 594\u2013623 (2013). https:\/\/doi.org\/10.1007\/s10664-013-9249-9","journal-title":"Empir. Softw. Eng."},{"key":"5_CR6","doi-asserted-by":"crossref","unstructured":"Bartz-Beielstein, T.: SPOT: an R package for automatic and interactive tuning of optimization algorithms by sequential parameter optimization. CoRR abs\/1006.4645 (2010)","DOI":"10.1007\/978-3-642-02538-9_14"},{"key":"5_CR7","unstructured":"Bellman, R.E.: Dynamic Programming. Princeton University Press (1957)"},{"key":"5_CR8","doi-asserted-by":"crossref","unstructured":"Bellman, R.E.: Adaptive Control Processes: A Guided Tour. Princeton University Press (1961)","DOI":"10.1515\/9781400874668"},{"key":"5_CR9","doi-asserted-by":"publisher","first-page":"281","DOI":"10.5555\/2503308.2188395","volume":"13","author":"J Bergstra","year":"2012","unstructured":"Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. J. Mach. Learn. Res. 13, 281\u2013305 (2012). https:\/\/doi.org\/10.5555\/2503308.2188395","journal-title":"J. Mach. Learn. Res."},{"key":"5_CR10","doi-asserted-by":"publisher","first-page":"207","DOI":"10.1016\/j.infsof.2018.08.010","volume":"104","author":"J Campos","year":"2018","unstructured":"Campos, J., Ge, Y., Albunian, N., Fraser, G., Eler, M., Arcuri, A.: An empirical evaluation of evolutionary algorithms for unit test suite generation. Inf. Softw. Technol. 104, 207\u2013235 (2018). https:\/\/doi.org\/10.1016\/j.infsof.2018.08.010","journal-title":"Inf. Softw. Technol."},{"key":"5_CR11","doi-asserted-by":"publisher","unstructured":"Daka, E., Campos, J., Fraser, G., Dorn, J., Weimer, W.: Modeling readability to improve unit tests. In: Proceedings ESEC\/FSE, pp. 107\u2013118. ACM (2015). https:\/\/doi.org\/10.1145\/2786805.2786838","DOI":"10.1145\/2786805.2786838"},{"key":"5_CR12","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.: Recent advances in differential evolution - an updated survey. Swarm Evol. Comput. 27, 1\u201330 (2016). https:\/\/doi.org\/10.1016\/J.SWEVO.2016.01.004","journal-title":"Swarm Evol. Comput."},{"issue":"2","key":"5_CR13","doi-asserted-by":"publisher","first-page":"124","DOI":"10.1109\/4235.771166","volume":"3","author":"AE Eiben","year":"1999","unstructured":"Eiben, A.E., Hinterding, R., Michalewicz, B.: Parameter control in evolutionary algorithms. IEEE Trans. Evol. Comput. 3(2), 124\u2013141 (1999). https:\/\/doi.org\/10.1109\/4235.771166","journal-title":"IEEE Trans. Evol. Comput."},{"key":"5_CR14","doi-asserted-by":"publisher","unstructured":"Eiben, A.E., Michalewicz, Z., Schoenauer, M., Smith, J.E.: Parameter control in evolutionary algorithms. In: Parameter Settings in Evolutionary Algorithms, Studies in Computational Intelligence, vol.\u00a054, pp. 19\u201346. Springer (2007). https:\/\/doi.org\/10.1007\/978-3-540-69432-8_2","DOI":"10.1007\/978-3-540-69432-8_2"},{"key":"5_CR15","doi-asserted-by":"publisher","unstructured":"Fraser, G., Arcuri, A.: Evosuite: automatic test suite generation for object-oriented software. In: Proceedings ESEC\/FSE, pp. 416\u2013419. ACM (2011). https:\/\/doi.org\/10.1145\/2025113.2025179","DOI":"10.1145\/2025113.2025179"},{"issue":"2","key":"5_CR16","doi-asserted-by":"publisher","first-page":"276","DOI":"10.1109\/TSE.2012.14","volume":"39","author":"G Fraser","year":"2013","unstructured":"Fraser, G., Arcuri, A.: Whole test suite generation. IEEE Trans. Software Eng. 39(2), 276\u2013291 (2013). https:\/\/doi.org\/10.1109\/TSE.2012.14","journal-title":"IEEE Trans. Software Eng."},{"key":"5_CR17","doi-asserted-by":"publisher","unstructured":"Fu, W., Menzies, T.: Easy over hard: a case study on deep learning. In: Proceedings ESEC\/FSE, pp. 49\u201360. ACM (2017). https:\/\/doi.org\/10.1145\/3106237.3106256","DOI":"10.1145\/3106237.3106256"},{"key":"5_CR18","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1016\/j.infsof.2016.04.017","volume":"76","author":"W Fu","year":"2016","unstructured":"Fu, W., Menzies, T., Shen, X.: Tuning for software analytics: is it really necessary? Inf. Softw. Technol. 76, 135\u2013146 (2016). https:\/\/doi.org\/10.1016\/j.infsof.2016.04.017","journal-title":"Inf. Softw. Technol."},{"key":"5_CR19","unstructured":"Fu, W., Nair, V., Menzies, T.: Why is differential evolution better than grid search for tuning defect predictors? CoRR abs\/1609.02613 (2016)"},{"key":"5_CR20","unstructured":"Herlihy, M., Shavit, N.: The Art of Multiprocessor Programming. Elsevier (2012)"},{"key":"5_CR21","doi-asserted-by":"publisher","unstructured":"Huang, C., Yao, X., Li, Y.: A survey of automatic parameter tuning methods for metaheuristics. IEEE Trans. Evol. Comput. 24(2), 201\u2013216 (2020). https:\/\/doi.org\/10.1109\/TEVC.2019.2921598","DOI":"10.1109\/TEVC.2019.2921598"},{"key":"5_CR22","doi-asserted-by":"publisher","first-page":"267","DOI":"10.1613\/JAIR.2861","volume":"36","author":"F Hutter","year":"2009","unstructured":"Hutter, F., Hoos, H.H., Leyton-Brown, K., St\u00fctzle, T.: ParamILS: an automatic algorithm configuration framework. J. Artif. Intell. Res. 36, 267\u2013306 (2009). https:\/\/doi.org\/10.1613\/JAIR.2861","journal-title":"J. Artif. Intell. Res."},{"key":"5_CR23","doi-asserted-by":"publisher","unstructured":"Kotelyanskii, A., Kapfhammer, G.M.: Parameter tuning for search-based test-data generation revisited: support for previous results. In: Proceedings QSIC, pp. 79\u201384. IEEE (2014). https:\/\/doi.org\/10.1109\/QSIC.2014.43","DOI":"10.1109\/QSIC.2014.43"},{"key":"5_CR24","doi-asserted-by":"publisher","unstructured":"Lemieux, C., Inala, J.P., Lahiri, S.K., Sen, S.: CodaMOSA: escaping coverage plateaus in test generation with pre-trained large language models. In: Proceedings ICSE, pp. 919\u2013931. IEEE (2023). https:\/\/doi.org\/10.1109\/ICSE48619.2023.00085","DOI":"10.1109\/ICSE48619.2023.00085"},{"key":"5_CR25","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1016\/j.orp.2016.09.002","volume":"3","author":"M L\u00f3pez-Ib\u00e1\u00f1ez","year":"2016","unstructured":"L\u00f3pez-Ib\u00e1\u00f1ez, M., Dubois-Lacoste, J., C\u00e1ceres, L.P., Birattari, M., St\u00fctzle, T.: The irace package: iterated racing for automatic algorithm configuration. Oper. Res. Perspect. 3, 43\u201358 (2016). https:\/\/doi.org\/10.1016\/j.orp.2016.09.002","journal-title":"Oper. Res. Perspect."},{"key":"5_CR26","doi-asserted-by":"publisher","unstructured":"Lukasczyk, S., Fraser, G.: Pynguin: automated unit test generation for Python. In: Proceedings ICSE Companion, pp. 168\u2013172. IEEE\/ACM (2022). https:\/\/doi.org\/10.1145\/3510454.3516829","DOI":"10.1145\/3510454.3516829"},{"key":"5_CR27","doi-asserted-by":"publisher","unstructured":"Lukasczyk, S., Kroi\u00df, F., Fraser, G.: Automated unit test generation for\u00a0Python. In: Proceedings of the SSBSE 2020. LNCS, vol. 12420, pp. 9\u201324. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59762-7_2","DOI":"10.1007\/978-3-030-59762-7_2"},{"key":"5_CR28","doi-asserted-by":"publisher","unstructured":"Lukasczyk, S., Kroi\u00df, F., Fraser, G.: An empirical study of automated unit test generation for Python. Empir. Softw. Eng. 28(2), 36:1\u201336:46 (2023). https:\/\/doi.org\/10.1007\/s10664-022-10248-w","DOI":"10.1007\/s10664-022-10248-w"},{"issue":"1","key":"5_CR29","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1214\/aoms\/1177730491","volume":"18","author":"HB Mann","year":"1947","unstructured":"Mann, H.B., Whitney, D.R.: On a test of whether one of two random variables is stochastically larger than the other. Ann. Math. Stat. 18(1), 50\u201360 (1947). https:\/\/doi.org\/10.1214\/aoms\/1177730491","journal-title":"Ann. Math. Stat."},{"issue":"2","key":"5_CR30","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1002\/stvr.294","volume":"14","author":"P McMinn","year":"2004","unstructured":"McMinn, P.: Search-based software test data generation: a survey. Softw. Test. Verification Reliab. 14(2), 105\u2013156 (2004). https:\/\/doi.org\/10.1002\/stvr.294","journal-title":"Softw. Test. Verification Reliab."},{"key":"5_CR31","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1016\/J.ASOC.2013.12.017","volume":"17","author":"E Montero","year":"2014","unstructured":"Montero, E., Riff, M., Neveu, B.: A beginner\u2019s guide to tuning methods. Appl. Soft Comput. 17, 39\u201351 (2014). https:\/\/doi.org\/10.1016\/J.ASOC.2013.12.017","journal-title":"Appl. Soft Comput."},{"key":"5_CR32","doi-asserted-by":"publisher","unstructured":"Mosayebi, M., Sodhi, M.: Tuning genetic algorithm parameters using design of experiments. In: Proceedings GECCO, pp. 1937\u20131944. ACM (2020). https:\/\/doi.org\/10.1145\/3377929.3398136","DOI":"10.1145\/3377929.3398136"},{"issue":"1\u20132","key":"5_CR33","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1007\/S10462-009-9137-2","volume":"33","author":"F Neri","year":"2010","unstructured":"Neri, F., Tirronen, V.: Recent advances in differential evolution: a survey and experimental analysis. Artif. Intelli. Rev. 33(1\u20132), 61\u2013106 (2010). https:\/\/doi.org\/10.1007\/S10462-009-9137-2","journal-title":"Artif. Intelli. Rev."},{"key":"5_CR34","doi-asserted-by":"publisher","unstructured":"Panichella, A., Kifetew, F.M., Tonella, P.: Reformulating branch coverage as a many-objective optimization problem. In: Proceedings ICST, pp. 1\u201310. IEEE Computer Society (2015). https:\/\/doi.org\/10.1109\/ICST.2015.7102604","DOI":"10.1109\/ICST.2015.7102604"},{"issue":"2","key":"5_CR35","doi-asserted-by":"publisher","first-page":"122","DOI":"10.1109\/TSE.2017.2663435","volume":"44","author":"A Panichella","year":"2018","unstructured":"Panichella, A., Kifetew, F.M., Tonella, P.: Automated test case generation as a many-objective optimisation problem with dynamic selection of the targets. IEEE Trans. Software Eng. 44(2), 122\u2013158 (2018). https:\/\/doi.org\/10.1109\/TSE.2017.2663435","journal-title":"IEEE Trans. Software Eng."},{"key":"5_CR36","doi-asserted-by":"publisher","unstructured":"Poulding, S.M., Clark, J.A., Waeselynck, H.: A principled evaluation of the effect of directed mutation on search-based statistical testing. In: Proceedings ICST Workshops, pp. 184\u2013193. IEEE Computer Society (2011). https:\/\/doi.org\/10.1109\/ICSTW.2011.36","DOI":"10.1109\/ICSTW.2011.36"},{"key":"5_CR37","doi-asserted-by":"publisher","unstructured":"Preuss, M., Rudolph, G., Wessing, S.: Tuning optimization algorithms for real-world problems by means of surrogate modeling. In: Proceedings GECCO, pp. 401\u2013408. ACM (2010). https:\/\/doi.org\/10.1145\/1830483.1830558","DOI":"10.1145\/1830483.1830558"},{"key":"5_CR38","doi-asserted-by":"publisher","unstructured":"Ribeiro, J.C.B., Zenha-Rela, M., de\u00a0Vega, F.F.: Adaptive evolutionary testing: an adaptive approach to search-based test case generation for object-oriented software. In: Proceedings NICSO. SCI, vol.\u00a0284, pp. 185\u2013197. Springer (2010). https:\/\/doi.org\/10.1007\/978-3-642-12538-6_16","DOI":"10.1007\/978-3-642-12538-6_16"},{"issue":"4","key":"5_CR39","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1023\/A:1008202821328","volume":"11","author":"R Storn","year":"1997","unstructured":"Storn, R., Price, K.V.: Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces. J. Glob. Optim. 11(4), 341\u2013359 (1997). https:\/\/doi.org\/10.1023\/A:1008202821328","journal-title":"J. Glob. Optim."},{"issue":"2","key":"5_CR40","doi-asserted-by":"publisher","first-page":"101","DOI":"10.3102\/10769986025002101","volume":"25","author":"A Vargha","year":"2000","unstructured":"Vargha, A., Delaney, H.D.: A critique and improvement of the CL common language effect size statistics of McGraw and Wong. J. Educ. Behav. Stat. 25(2), 101\u2013132 (2000). https:\/\/doi.org\/10.3102\/10769986025002101","journal-title":"J. Educ. Behav. Stat."},{"key":"5_CR41","doi-asserted-by":"publisher","unstructured":"Villalobos-Arias, L., Quesada-L\u00f3pez, C.: Comparative study of random search hyper-parameter tuning for software effort estimation. In: Proceedings PROMISE, pp. 21\u201329. ACM (2021). https:\/\/doi.org\/10.1145\/3475960.3475986","DOI":"10.1145\/3475960.3475986"},{"key":"5_CR42","doi-asserted-by":"publisher","unstructured":"Villalobos-Arias, L., Quesada-L\u00f3pez, C., Guevara-Coto, J., Mart\u00ednez, A., Jenkins, M.: Evaluating hyper-parameter tuning using random search in support vector machines for software effort estimation. In: Proceedings PROMISE, pp. 31\u201340. ACM (2020). https:\/\/doi.org\/10.1145\/3416508.3417121","DOI":"10.1145\/3416508.3417121"},{"issue":"6","key":"5_CR43","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10664-021-10024-2","volume":"26","author":"S Zamani","year":"2021","unstructured":"Zamani, S., Hemmati, H.: A pragmatic approach for hyper-parameter tuning in search-based test case generation. Empir. Softw. Eng. 26(6), 1\u201335 (2021). https:\/\/doi.org\/10.1007\/s10664-021-10024-2","journal-title":"Empir. Softw. Eng."}],"container-title":["Lecture Notes in Computer Science","Search-Based Software Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-24839-8_5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T05:45:04Z","timestamp":1780897504000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-24839-8_5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032248381","9783032248398"],"references-count":43,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-24839-8_5","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"30 April 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"SSBSE","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Symposium on Search Based Software Engineering","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Seoul","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Korea (Republic of)","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 November 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 November 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ssbse2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conf.researchr.org\/home\/ssbse-2025","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}