{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T20:39:03Z","timestamp":1757623143373,"version":"3.44.0"},"publisher-location":"Cham","reference-count":41,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032042064","type":"print"},{"value":"9783032042071","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,9,9]],"date-time":"2025-09-09T00:00:00Z","timestamp":1757376000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,9]],"date-time":"2025-09-09T00:00:00Z","timestamp":1757376000000},"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\/978-3-032-04207-1_15","type":"book-chapter","created":{"date-parts":[[2025,9,9]],"date-time":"2025-09-09T03:28:50Z","timestamp":1757388530000},"page":"214-230","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Teaching Software Engineering for\u00a0Artificial Intelligence: An Experience Report"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9337-5116","authenticated-orcid":false,"given":"Fabio","family":"Palomba","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0002-5394-8148","authenticated-orcid":false,"given":"Gianmario","family":"Voria","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0001-0758-3988","authenticated-orcid":false,"given":"Alessandra","family":"Parziale","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0003-1425-9398","authenticated-orcid":false,"given":"Viviana","family":"Pentangelo","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1860-8404","authenticated-orcid":false,"given":"Antonio Della","family":"Porta","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1485-4560","authenticated-orcid":false,"given":"Vincenzo De","family":"Martino","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8088-1001","authenticated-orcid":false,"given":"Gilberto","family":"Recupito","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2567-440X","authenticated-orcid":false,"given":"Giammaria","family":"Giordano","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,9,9]]},"reference":[{"key":"15_CR1","doi-asserted-by":"crossref","unstructured":"Abdessalem, R.B., Nejati, S., Briand, L.C., Stifter, T.: Testing vision-based control systems using learnable evolutionary algorithms. In: Proceedings of the 40th International Conference on Software Engineering, pp. 1016\u20131026 (2018)","DOI":"10.1145\/3180155.3180160"},{"issue":"4","key":"15_CR2","doi-asserted-by":"publisher","first-page":"2202","DOI":"10.1109\/TSE.2022.3214764","volume":"49","author":"D Albuquerque","year":"2022","unstructured":"Albuquerque, D., et al.: Managing technical debt using intelligent techniques-a systematic mapping study. IEEE Trans. Software Eng. 49(4), 2202\u20132220 (2022)","journal-title":"IEEE Trans. Software Eng."},{"issue":"6","key":"15_CR3","doi-asserted-by":"publisher","first-page":"742","DOI":"10.1109\/TSE.2009.52","volume":"36","author":"S Ali","year":"2009","unstructured":"Ali, S., Briand, L.C., Hemmati, H., Panesar-Walawege, R.K.: A systematic review of the application and empirical investigation of search-based test case generation. IEEE Trans. Software Eng. 36(6), 742\u2013762 (2009)","journal-title":"IEEE Trans. Software Eng."},{"issue":"8","key":"15_CR4","doi-asserted-by":"publisher","first-page":"1978","DOI":"10.1016\/j.jss.2013.02.061","volume":"86","author":"S Anand","year":"2013","unstructured":"Anand, S., et al.: An orchestrated survey of methodologies for automated software test case generation. J. Syst. Softw. 86(8), 1978\u20132001 (2013)","journal-title":"J. Syst. Softw."},{"key":"15_CR5","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1016\/j.infsof.2018.12.009","volume":"108","author":"MI Azeem","year":"2019","unstructured":"Azeem, M.I., Palomba, F., Shi, L., Wang, Q.: Machine learning techniques for code smell detection: a systematic literature review and meta-analysis. Inf. Softw. Technol. 108, 115\u2013138 (2019)","journal-title":"Inf. Softw. Technol."},{"key":"15_CR6","doi-asserted-by":"crossref","unstructured":"Beede, E., et al.: A human-centered evaluation of a deep learning system deployed in clinics for the detection of diabetic retinopathy. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1\u201312 (2020)","DOI":"10.1145\/3313831.3376718"},{"key":"15_CR7","doi-asserted-by":"crossref","unstructured":"Bocu, R., Baicoianu, A., Kerestely, A.: An extended survey concerning the significance of artificial intelligence and machine learning techniques for bug triage and management. IEEE Access (2023)","DOI":"10.1109\/ACCESS.2023.3329732"},{"key":"15_CR8","doi-asserted-by":"crossref","unstructured":"Bosch, J., Olsson, H.H., Crnkovic, I.: Engineering ai systems: a research agenda. Artificial Intelligence Paradigms for Smart Cyber-Physical Systems, pp. 1\u201319 (2021)","DOI":"10.4018\/978-1-7998-5101-1.ch001"},{"key":"15_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.jss.2021.110933","volume":"176","author":"M Brunetto","year":"2021","unstructured":"Brunetto, M., Denaro, G., Mariani, L., Pezz\u00e8, M.: On introducing automatic test case generation in practice: a success story and lessons learned. J. Syst. Softw. 176, 110933 (2021)","journal-title":"J. Syst. Softw."},{"key":"15_CR10","volume-title":"Machine learning engineering","author":"A Burkov","year":"2020","unstructured":"Burkov, A.: Machine learning engineering, vol. 1. True Positive Incorporated Montreal, QC, Canada (2020)"},{"key":"15_CR11","doi-asserted-by":"crossref","unstructured":"Byun, T., Sharma, V., Vijayakumar, A., Rayadurgam, S., Cofer, D.: Input prioritization for testing neural networks. In: 2019 IEEE International Conference On Artificial Intelligence Testing (AITest), pp. 63\u201370. IEEE (2019)","DOI":"10.1109\/AITest.2019.000-6"},{"key":"15_CR12","doi-asserted-by":"crossref","unstructured":"Chakraborty, J., Majumder, S., Menzies, T.: Bias in machine learning software: Why? how? what to do? In: Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering, pp. 429\u2013440 (2021)","DOI":"10.1145\/3468264.3468537"},{"issue":"4","key":"15_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3583561","volume":"32","author":"Z Chen","year":"2023","unstructured":"Chen, Z., Zhang, J.M., Sarro, F., Harman, M.: A comprehensive empirical study of bias mitigation methods for machine learning classifiers. ACM Trans. Softw. Eng. Methodol. 32(4), 1\u201330 (2023)","journal-title":"ACM Trans. Softw. Eng. Methodol."},{"key":"15_CR14","doi-asserted-by":"crossref","unstructured":"Chenoweth, S., Linos, P.K.: Teaching machine learning as part of agile software engineering. IEEE Trans. Educ. (2023)","DOI":"10.1109\/TE.2023.3337343"},{"key":"15_CR15","doi-asserted-by":"crossref","unstructured":"Diosan, L., Motogna, S.: Artificial intelligence meets software engineering in the classroom. In: Proceedings of the 1st ACM SIGSOFT International Workshop on Education through Advanced Software Engineering and Artificial Intelligence, pp. 35\u201338 (2019)","DOI":"10.1145\/3340435.3342718"},{"key":"15_CR16","doi-asserted-by":"publisher","unstructured":"Fioravanti, M.L., Sena, B., Paschoal, L.N., Silva, L.R., Allian, A.P., Nakagawa, E.Y., Souza, S.R., Isotani, S., Barbosa, E.F.: Integrating project based learning and project management for software engineering teaching: An experience report. In: Proceedings of the 49th ACM Technical Symposium on Computer Science Education, pp. 806\u2013811. SIGCSE \u201918. Association for Computing Machinery, New York (2018). https:\/\/doi.org\/10.1145\/3159450.3159599","DOI":"10.1145\/3159450.3159599"},{"key":"15_CR17","doi-asserted-by":"crossref","unstructured":"Galhotra, S., Brun, Y., Meliou, A.: Fairness testing: testing software for discrimination. In: Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering, pp. 498\u2013510 (2017)","DOI":"10.1145\/3106237.3106277"},{"issue":"6","key":"15_CR18","doi-asserted-by":"publisher","first-page":"1276","DOI":"10.1109\/TSE.2011.103","volume":"38","author":"T Hall","year":"2011","unstructured":"Hall, T., Beecham, S., Bowes, D., Gray, D., Counsell, S.: A systematic literature review on fault prediction performance in software engineering. IEEE Trans. Software Eng. 38(6), 1276\u20131304 (2011)","journal-title":"IEEE Trans. Software Eng."},{"key":"15_CR19","doi-asserted-by":"crossref","unstructured":"Hulten, G.: Building intelligent systems: a guide to machine learning engineering. Apress (2018)","DOI":"10.1007\/978-1-4842-3432-7"},{"key":"15_CR20","doi-asserted-by":"crossref","unstructured":"K\u00e4stner, C., Kang, E.: Teaching software engineering for ai-enabled systems. In: Proceedings of the ACM\/IEEE 42nd International Conference on Software Engineering: Software Engineering Education and Training, pp. 45\u201348 (2020)","DOI":"10.1145\/3377814.3381714"},{"key":"15_CR21","doi-asserted-by":"crossref","unstructured":"Kim, J., Feldt, R., Yoo, S.: Guiding deep learning system testing using surprise adequacy. In: 2019 IEEE\/ACM 41st International Conference on Software Engineering (ICSE). pp. 1039\u20131049. IEEE (2019)","DOI":"10.1109\/ICSE.2019.00108"},{"key":"15_CR22","doi-asserted-by":"crossref","unstructured":"Lanubile, F., Mart\u00ednez-Fern\u00e1ndez, S., Quaranta, L.: Teaching mlops in higher education through project-based learning. In: 2023 IEEE\/ACM 45th International Conference on Software Engineering: Software Engineering Education and Training (ICSE-SEET). pp. 95\u2013100. IEEE (2023)","DOI":"10.1109\/ICSE-SEET58685.2023.00015"},{"key":"15_CR23","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1016\/j.infsof.2016.11.009","volume":"83","author":"T Mariani","year":"2017","unstructured":"Mariani, T., Vergilio, S.R.: A systematic review on search-based refactoring. Inf. Softw. Technol. 83, 14\u201334 (2017)","journal-title":"Inf. Softw. Technol."},{"issue":"2","key":"15_CR24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3487043","volume":"31","author":"S Mart\u00ednez-Fern\u00e1ndez","year":"2022","unstructured":"Mart\u00ednez-Fern\u00e1ndez, S., Bogner, J., Franch, X., Oriol, M., Siebert, J., Trendowicz, A., Vollmer, A.M., Wagner, S.: Software engineering for ai-based systems: a survey. ACM Trans. Softw. Eng. Methodol. (TOSEM) 31(2), 1\u201359 (2022)","journal-title":"ACM Trans. Softw. Eng. Methodol. (TOSEM)"},{"issue":"1","key":"15_CR25","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1109\/MS.2019.2954841","volume":"37","author":"T Menzies","year":"2020","unstructured":"Menzies, T.: The five laws of se for ai. IEEE Softw. 37(1), 81\u201385 (2020). https:\/\/doi.org\/10.1109\/MS.2019.2954841","journal-title":"IEEE Softw."},{"issue":"5","key":"15_CR26","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1109\/MS.2018.290111035","volume":"35","author":"T Menzies","year":"2018","unstructured":"Menzies, T., Zimmermann, T.: Software analytics: What\u2019s next? IEEE Softw. 35(5), 64\u201370 (2018)","journal-title":"IEEE Softw."},{"key":"15_CR27","unstructured":"Miller, C.C.: Can an algorithm hire better than a human. The New York Times 25 (2015)"},{"key":"15_CR28","unstructured":"Nemoto, T., Beglar, D.: Likert-scale questionnaires. In: JALT 2013 Conference Proceedings, pp.\u00a01\u20138 (2014)"},{"issue":"8","key":"15_CR29","doi-asserted-by":"publisher","first-page":"2749","DOI":"10.3390\/app10082749","volume":"10","author":"J Ni","year":"2020","unstructured":"Ni, J., Chen, Y., Chen, Y., Zhu, J., Ali, D., Cao, W.: A survey on theories and applications for self-driving cars based on deep learning methods. Appl. Sci. 10(8), 2749 (2020)","journal-title":"Appl. Sci."},{"key":"15_CR30","unstructured":"Palomba, F., Voria, G., Parziale, A., Pentangelo, V., Della\u00a0Porta, A., De\u00a0Martino, V., Recupito, G., Giammaria, G.: Online appendix - teaching software quality assurance for artificial intelligence: An experience report https:\/\/figshare.com\/s\/c0797c1cd4569ce3d285"},{"issue":"4","key":"15_CR31","doi-asserted-by":"publisher","first-page":"2426","DOI":"10.1109\/TSE.2022.3220713","volume":"49","author":"K Peng","year":"2022","unstructured":"Peng, K., Chakraborty, J., Menzies, T.: Fairmask: better fairness via model-based rebalancing of protected attributes. IEEE Trans. Software Eng. 49(4), 2426\u20132439 (2022)","journal-title":"IEEE Trans. Software Eng."},{"key":"15_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.jss.2024.112151","author":"G Recupito","year":"2024","unstructured":"Recupito, G., et al.: Technical debt in ai-enabled systems: on the prevalence, severity, impact, and management strategies for code and architecture. J. Syst. Softw. (2024). https:\/\/doi.org\/10.1016\/j.jss.2024.112151","journal-title":"J. Syst. Softw."},{"issue":"6","key":"15_CR33","doi-asserted-by":"publisher","first-page":"5193","DOI":"10.1007\/s10664-020-09881-0","volume":"25","author":"V Riccio","year":"2020","unstructured":"Riccio, V., Jahangirova, G., Stocco, A., Humbatova, N., Weiss, M., Tonella, P.: Testing machine learning based systems: a systematic mapping. Empir. Softw. Eng. 25(6), 5193\u20135254 (2020). https:\/\/doi.org\/10.1007\/s10664-020-09881-0","journal-title":"Empir. Softw. Eng."},{"key":"15_CR34","unstructured":"Sculley, D., et al.: Hidden technical debt in machine learning systems. Advances in neural information processing systems 28 (2015)"},{"key":"15_CR35","unstructured":"Smith, J.: Machine Learning Systems: Designs that Scale. Simon and Schuster (2018)"},{"key":"15_CR36","doi-asserted-by":"crossref","unstructured":"Sperling, A., Lickerman, D.: Integrating ai and machine learning in software engineering course for high school students. In: Proceedings of the 17th ACM Annual Conference on Innovation and Technology in Computer Science Education, pp. 244\u2013249 (2012)","DOI":"10.1145\/2325296.2325354"},{"issue":"9","key":"15_CR37","first-page":"1857","volume":"47","author":"Z Wan","year":"2019","unstructured":"Wan, Z., Xia, X., Lo, D., Murphy, G.C.: How does machine learning change software development practices? IEEE Trans. Software Eng. 47(9), 1857\u20131871 (2019)","journal-title":"IEEE Trans. Software Eng."},{"key":"15_CR38","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1016\/j.patrec.2020.07.042","volume":"141","author":"P Wang","year":"2021","unstructured":"Wang, P., Fan, E., Wang, P.: Comparative analysis of image classification algorithms based on traditional machine learning and deep learning. Pattern Recogn. Lett. 141, 61\u201367 (2021)","journal-title":"Pattern Recogn. Lett."},{"issue":"3","key":"15_CR39","first-page":"1","volume":"31","author":"Y Yang","year":"2022","unstructured":"Yang, Y., Xia, X., Lo, D., Bi, T., Grundy, J., Yang, X.: Predictive models in software engineering: challenges and opportunities. ACM Trans. Softw. Eng. Methodol. (TOSEM) 31(3), 1\u201372 (2022)","journal-title":"ACM Trans. Softw. Eng. Methodol. (TOSEM)"},{"issue":"10s","key":"15_CR40","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3505243","volume":"54","author":"Y Yang","year":"2022","unstructured":"Yang, Y., Xia, X., Lo, D., Grundy, J.: A survey on deep learning for software engineering. ACM Comput. Surv. (CSUR) 54(10s), 1\u201373 (2022)","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"15_CR41","doi-asserted-by":"crossref","unstructured":"Zhou, J., Chen, F.: Human and Machine Learning. Springer (2018)","DOI":"10.1007\/978-3-319-90403-0"}],"container-title":["Lecture Notes in Computer Science","Software Engineering and Advanced Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-04207-1_15","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,9]],"date-time":"2025-09-09T03:29:05Z","timestamp":1757388545000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-04207-1_15"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,9]]},"ISBN":["9783032042064","9783032042071"],"references-count":41,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-04207-1_15","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,9]]},"assertion":[{"value":"9 September 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"SEAA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Euromicro Conference on Software Engineering and Advanced Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Salerno","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","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":"10 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"51","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"seaa-12025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/dsd-seaa.com\/seaa2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}