{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,31]],"date-time":"2026-10-31T20:30:59Z","timestamp":1793478659170,"version":"4.5.0"},"reference-count":58,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2023,1,7]],"date-time":"2023-01-07T00:00:00Z","timestamp":1673049600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,1,7]],"date-time":"2023-01-07T00:00:00Z","timestamp":1673049600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100004359","name":"Swedish Research Council","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100004359","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100005760","name":"University of Gothenburg","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100005760","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Requirements Eng"],"published-print":{"date-parts":[[2023,6]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Systems that rely on Machine Learning (ML systems) have differing demands on quality\u2014known as non-functional requirements (NFRs)\u2014from traditional systems. NFRs for ML systems may differ in their definition, measurement, scope, and comparative importance. Despite the importance of NFRs in ensuring the quality ML systems, our understanding of all of these aspects is lacking compared to our understanding of NFRs in traditional domains. We have conducted interviews and a survey to understand how NFRs for ML systems are perceived among practitioners from both industry and academia. We have identified the degree of importance that practitioners place on different NFRs, including cases where practitioners are in agreement or have differences of opinion. We explore how NFRs are defined and measured over different aspects of a ML system (i.e., model, data, or whole system). We also identify challenges associated with NFR definition and measurement. Finally, we explore differences in perspective between practitioners in industry, academia, or a blended context. This knowledge illustrates how NFRs for ML systems are treated in current practice, and helps to guide future RE for ML efforts.<\/jats:p>","DOI":"10.1007\/s00766-022-00395-3","type":"journal-article","created":{"date-parts":[[2023,1,7]],"date-time":"2023-01-07T05:05:48Z","timestamp":1673067948000},"page":"283-316","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":60,"title":["Non-functional requirements for machine learning: understanding current use and challenges among practitioners"],"prefix":"10.1007","volume":"28","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0670-1524","authenticated-orcid":false,"given":"Khan Mohammad","family":"Habibullah","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gregory","family":"Gay","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jennifer","family":"Horkoff","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,1,7]]},"reference":[{"issue":"17","key":"395_CR1","doi-asserted-by":"publisher","first-page":"8700","DOI":"10.3390\/app12178700","volume":"12","author":"MA Ali","year":"2022","unstructured":"Ali MA, Yap NK, Ghani AAA, Zulzalil H, Admodisastro NI, Najafabadi AA (2022) A systematic mapping of quality models for AI systems, software and components. Appl Sci 12(17):8700","journal-title":"Appl Sci"},{"key":"395_CR2","first-page":"1","volume":"1","author":"D Ameller","year":"2019","unstructured":"Ameller D, Franch X, G\u00f3mez C, Mart\u00ednez-Fern\u00e1ndez S, Ara\u00fajo J, Biffl S, Cabot J, Cortellessa V, M\u00e9ndez D, Moreira A et al (2019) Dealing with non-functional requirements in model-driven development: a survey. IEEE Trans Softw Eng 1:1\u20132","journal-title":"IEEE Trans Softw Eng"},{"key":"395_CR3","doi-asserted-by":"crossref","unstructured":"Amershi S, Begel A, Bird C, DeLine R, Gall H, Kamar E, Nagappan N, Nushi B, Zimmermann T (2019) Software engineering for machine learning: a case study. In: 2019 IEEE\/ACM 41st international conference on software engineering: software engineering in practice (ICSE-SEIP). IEEE, pp 291\u2013300","DOI":"10.1109\/ICSE-SEIP.2019.00042"},{"key":"395_CR4","doi-asserted-by":"crossref","unstructured":"Anisetti M, Ardagna CA, Damiani E, Panero PG (2020) A methodology for non-functional property evaluation of machine learning models. In: Proceedings of the 12th international conference on management of digital EcoSystems, pp 38\u201345","DOI":"10.1145\/3415958.3433101"},{"key":"395_CR5","doi-asserted-by":"crossref","unstructured":"Khan Mohammad Habibullah and Jennifer Horkoff. Non-functional requirements for machine learning: understanding current use and challenges in industry. In: 2021 IEEE 29th International Requirements Engineering Conference (RE), pages 13\u201323. IEEE, 2021.","DOI":"10.1109\/RE51729.2021.00009"},{"issue":"1","key":"395_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3293454","volume":"28","author":"C Arora","year":"2019","unstructured":"Arora C, Sabetzadeh M, Nejati S, Briand L (2019) An active learning approach for improving the accuracy of automated domain model extraction. ACM Trans Softw Eng Methodol (TOSEM) 28(1):1\u201334","journal-title":"ACM Trans Softw Eng Methodol (TOSEM)"},{"key":"395_CR7","doi-asserted-by":"crossref","unstructured":"Arpteg A, Brinne B, Crnkovic-Friis L, Bosch J (2018) Software engineering challenges of deep learning. In: 2018 44th Euromicro conference on software engineering and advanced applications (SEAA). IEEE, pp 50\u201359","DOI":"10.1109\/SEAA.2018.00018"},{"key":"395_CR8","doi-asserted-by":"crossref","unstructured":"Balasubramaniam N, Kauppinen M, Hiekkanen K, Kujala S (2022) Transparency and explainability of AI systems: ethical guidelines in practice. In: International working conference on requirements engineering: foundation for software quality. Springer, Berlin, pp 3\u201318","DOI":"10.1007\/978-3-030-98464-9_1"},{"key":"395_CR9","doi-asserted-by":"crossref","unstructured":"Belani H, Vukovic M, Car \u017d (2019) Requirements engineering challenges in building AI-based complex systems. In: 2019 IEEE 27th international requirements engineering conference workshops (REW). IEEE, pp 252\u2013255","DOI":"10.1109\/REW.2019.00051"},{"key":"395_CR10","doi-asserted-by":"crossref","unstructured":"Berntsson\u00a0Svensson R, Regnell B (2015) A case study evaluation of the guideline-supported QUPER model for elicitation of quality requirements. In: International working conference on requirements engineering: foundation for software quality. Springer, Berlin, pp 230\u2013246","DOI":"10.1007\/978-3-319-16101-3_15"},{"key":"395_CR11","doi-asserted-by":"crossref","unstructured":"Berry DM (2022) Requirements engineering for artificial intelligence: what is a requirements specification for an artificial intelligence? In: International working conference on requirements engineering: foundation for software quality. Springer, Berlin, pp 19\u201325","DOI":"10.1007\/978-3-030-98464-9_2"},{"key":"395_CR12","doi-asserted-by":"crossref","unstructured":"Bibal A, Lognoul M, de Streel A, Fr\u00e9nay B (2020) Legal requirements on explainability in machine learning. Artif Intell Law 1\u201321","DOI":"10.1007\/s10506-020-09270-4"},{"key":"395_CR13","unstructured":"Binns R (2018) Fairness in machine learning: lessons from political philosophy. In: Conference on fairness, accountability and transparency. PMLR, pp 149\u2013159"},{"key":"395_CR14","unstructured":"Biran O, Cotton C (2017) Explanation and justification in machine learning: a survey. In IJCAI-17 workshop on explainable AI (XAI), vol 8, pp 8\u201313"},{"key":"395_CR15","doi-asserted-by":"crossref","unstructured":"Bonawitz K, Ivanov V, Kreuter B, Marcedone A, McMahan HB, Patel S, Ramage D, Segal A, Seth K (2017) Practical secure aggregation for privacy-preserving machine learning. In: proceedings of the 2017 ACM SIGSAC conference on computer and communications security, pp 1175\u20131191","DOI":"10.1145\/3133956.3133982"},{"key":"395_CR16","doi-asserted-by":"crossref","unstructured":"Brun Y, Meliou A (2018) Software fairness. In: Proceedings of the 2018 26th ACM joint meeting on european software engineering conference and symposium on the foundations of software engineering, pp 754\u2013759","DOI":"10.1145\/3236024.3264838"},{"key":"395_CR17","doi-asserted-by":"crossref","unstructured":"Cailliau A, Van\u00a0Lamsweerde A (2015) Handling knowledge uncertainty in risk-based requirements engineering. In: 2015 IEEE 23rd international requirements engineering conference (RE). IEEE, pp 106\u2013115","DOI":"10.1109\/RE.2015.7320413"},{"key":"395_CR18","doi-asserted-by":"crossref","unstructured":"Cavano JP, McCall JA (1978) A framework for the measurement of software quality. In: Proceedings of the software quality assurance workshop on functional and performance issues, pp 133\u2013139","DOI":"10.1145\/953579.811113"},{"issue":"3","key":"395_CR19","doi-asserted-by":"publisher","first-page":"231","DOI":"10.1136\/bmjqs-2018-008370","volume":"28","author":"R Challen","year":"2019","unstructured":"Challen R, Denny J, Pitt M, Gompels L, Edwards T, Tsaneva-Atanasova K (2019) Artificial intelligence, bias and clinical safety. BMJ Qual Saf 28(3):231\u2013237","journal-title":"BMJ Qual Saf"},{"issue":"4","key":"395_CR20","doi-asserted-by":"publisher","first-page":"493","DOI":"10.1007\/s00766-020-00333-1","volume":"25","author":"L Chazette","year":"2020","unstructured":"Chazette L, Schneider K (2020) Explainability as a non-functional requirement: challenges and recommendations. Requir Eng 25(4):493\u2013514","journal-title":"Requir Eng"},{"key":"395_CR21","volume-title":"Non-functional requirements in software engineering","author":"L Chung","year":"2012","unstructured":"Chung L, Nixon BA, Yu E, Mylopoulos J (2012) Non-functional requirements in software engineering, vol 5. Springer, Berlin"},{"issue":"2","key":"395_CR22","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1007\/s00766-007-0045-1","volume":"12","author":"J Cleland-Huang","year":"2007","unstructured":"Cleland-Huang J, Settimi R, Zou X, Solc P (2007) Automated classification of non-functional requirements. Requir Eng 12(2):103\u2013120","journal-title":"Requir Eng"},{"key":"395_CR23","volume-title":"Research design: qualitative, quantitative, and mixed methods approaches","author":"JW Creswell","year":"2017","unstructured":"Creswell JW, Creswell JD (2017) Research design: qualitative, quantitative, and mixed methods approaches. Sage publications, Beverly Hills"},{"issue":"4","key":"395_CR24","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1109\/MS.2020.2986047","volume":"37","author":"F Dalpiaz","year":"2020","unstructured":"Dalpiaz F, Niu N (2020) Requirements engineering in the days of artificial intelligence. IEEE Softw 37(4):7\u201310","journal-title":"IEEE Softw"},{"key":"395_CR25","doi-asserted-by":"crossref","unstructured":"Doerr J, Kerkow D, Koenig T, Olsson T, Suzuki T (2005) Non-functional requirements in industry-three case studies adopting an experience-based NFR method. In: 13th IEEE international conference on requirements engineering (RE\u201905). IEEE, pp 373\u2013382","DOI":"10.1109\/RE.2005.47"},{"key":"395_CR26","doi-asserted-by":"crossref","unstructured":"Eckhardt J, Vogelsang A, Fern\u00e1ndez DM (2016) Are\u201c non-functional\u201d requirements really non-functional? An investigation of non-functional requirements in practice. In: Proceedings of the 38th international conference on software engineering, pp 832\u2013842","DOI":"10.1145\/2884781.2884788"},{"key":"395_CR27","doi-asserted-by":"crossref","unstructured":"Felzmann H, Villaronga EF, Lutz C, Tam\u00f2-Larrieux A (2019) Transparency you can trust: transparency requirements for artificial intelligence between legal norms and contextual concerns. Big Data Soc 6(1):2053951719860542","DOI":"10.1177\/2053951719860542"},{"key":"395_CR28","doi-asserted-by":"crossref","unstructured":"Friedler SA, Scheidegger C, Venkatasubramanian S, Choudhary S, Hamilton EP, Roth D (2019) A comparative study of fairness-enhancing interventions in machine learning. In: Proceedings of the conference on fairness, accountability, and transparency, pp 329\u2013338","DOI":"10.1145\/3287560.3287589"},{"key":"395_CR29","doi-asserted-by":"crossref","unstructured":"Galster M, Bucherer E (2008) A taxonomy for identifying and specifying non-functional requirements in service-oriented development. In: 2008 IEEE congress on services-part I. IEEE, pp 345\u2013352","DOI":"10.1109\/SERVICES-1.2008.51"},{"key":"395_CR30","doi-asserted-by":"crossref","unstructured":"Glinz M (2007) On non-functional requirements. In: 15th IEEE international requirements engineering conference (RE 2007). IEEE, pp 21\u201326","DOI":"10.1109\/RE.2007.45"},{"key":"395_CR31","doi-asserted-by":"crossref","unstructured":"Habibullah KM, Gay G, Horkoff J (2022) Non-functional requirements for machine learning: an exploration of system scope and interest. In: SE4RAI workshop, 44th international conference on software engineering (ICSE 2022), (accepted)","DOI":"10.1145\/3526073.3527589"},{"key":"395_CR32","unstructured":"Hawkins R, Paterson C, Picardi C, Jia Y, Calinescu R, Habli I (2021) Guidance on the assurance of machine learning in autonomous systems (AMLAS). arXiv preprint arXiv:2102.01564"},{"key":"395_CR33","doi-asserted-by":"crossref","unstructured":"Heyn H-M, Knauss E, Muhammad AP, Erikssonz O, Linder J, Subbiah P, Pradhan SK, Tungal S (2021) Requirement engineering challenges for AI-intense systems development. In: 1st Workshop on AI engineering\u2014software engineering for AI (WAIN2021). IEEE","DOI":"10.1109\/WAIN52551.2021.00020"},{"key":"395_CR34","doi-asserted-by":"crossref","unstructured":"Horkoff J (2019) Non-functional requirements for machine learning: challenges and new directions. In: 2019 IEEE 27th international requirements engineering conference (RE). IEEE, pp 386\u2013391","DOI":"10.1109\/RE.2019.00050"},{"key":"395_CR35","doi-asserted-by":"crossref","unstructured":"Ishikawa F, Yoshioka N (2019) How do engineers perceive difficulties in engineering of machine-learning systems?-Questionnaire survey. In: 2019 IEEE\/ACM Joint 7th international workshop on conducting empirical studies in industry (CESI) and 6th international workshop on software engineering research and industrial practice (SER &IP). IEEE, pp 2\u20139","DOI":"10.1109\/CESSER-IP.2019.00009"},{"key":"395_CR36","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1007\/978-3-030-67084-9_6","volume-title":"Lean and agile software development","author":"A Jarz\u0119bowicz","year":"2021","unstructured":"Jarz\u0119bowicz A, Weichbroth P (2021) A systematic literature review on implementing non-functional requirements in agile software development: issues and facilitating practices. In: Przyby\u0142ek A, Miler J, Poth A, Riel A (eds) Lean and agile software development. Springer, Cham, pp 91\u2013110"},{"key":"395_CR37","doi-asserted-by":"crossref","unstructured":"Kamishima T, Akaho S, Sakuma J (2021) Fairness-aware learning through regularization approach. In: 2011 IEEE 11th international conference on data mining workshops. IEEE, pp 643\u2013650","DOI":"10.1109\/ICDMW.2011.83"},{"key":"395_CR38","doi-asserted-by":"crossref","unstructured":"Kaur H, ASU G, Sharma A (2014) Non-functional requirements research: survey. Int J Sci Eng Appl 3(6)","DOI":"10.7753\/IJSEA0306.1003"},{"key":"395_CR39","doi-asserted-by":"crossref","unstructured":"Lwakatare LE, Raj A, Bosch J, Olsson HH, Crnkovic I (2019) A taxonomy of software engineering challenges for machine learning systems: an empirical investigation. In: International conference on agile software development. Springer, Cham, pp 227\u2013243","DOI":"10.1007\/978-3-030-19034-7_14"},{"issue":"2","key":"395_CR40","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 AM, Wagner S (2022) Software engineering for AI-based systems: a survey. ACM Trans Softw Eng Methodol (TOSEM) 31(2):1\u201359","journal-title":"ACM Trans Softw Eng Methodol (TOSEM)"},{"issue":"6","key":"395_CR41","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3457607","volume":"54","author":"N Mehrabi","year":"2021","unstructured":"Mehrabi N, Morstatter F, Saxena N, Lerman K, Galstyan A (2021) A survey on bias and fairness in machine learning. ACM Comput Surv (CSUR) 54(6):1\u201335","journal-title":"ACM Comput Surv (CSUR)"},{"key":"395_CR42","doi-asserted-by":"crossref","unstructured":"Mohassel P, Zhang Y (2017) Secureml: a system for scalable privacy-preserving machine learning. In: 2017 IEEE symposium on security and privacy (SP). IEEE, pp 19\u201338","DOI":"10.1109\/SP.2017.12"},{"key":"395_CR43","doi-asserted-by":"crossref","unstructured":"Nakamichi K, Ohashi K, Namba I, Yamamoto R, Aoyama M, Joeckel L, Siebert J, Heidrich J (2020) Requirements-driven method to determine quality characteristics and measurements for machine learning software and its evaluation. In: 2020 IEEE 28th international requirements engineering conference (RE). IEEE, pp 260\u2013270","DOI":"10.1109\/RE48521.2020.00036"},{"issue":"2","key":"395_CR44","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1007\/s00766-020-00343-z","volume":"26","author":"S Nalchigar","year":"2021","unstructured":"Nalchigar S, Eric Yu, Keshavjee K (2021) Modeling machine learning requirements from three perspectives: a case report from the healthcare domain. Requir Eng 26(2):237\u2013254","journal-title":"Requir Eng"},{"issue":"6464","key":"395_CR45","doi-asserted-by":"publisher","first-page":"447","DOI":"10.1126\/science.aax2342","volume":"366","author":"Z Obermeyer","year":"2019","unstructured":"Obermeyer Z, Powers B, Vogeli C, Mullainathan S (2019) Dissecting racial bias in an algorithm used to manage the health of populations. Science 366(6464):447\u2013453","journal-title":"Science"},{"issue":"4","key":"395_CR46","doi-asserted-by":"publisher","first-page":"445","DOI":"10.1109\/TSE.2012.52","volume":"39","author":"A Perini","year":"2012","unstructured":"Perini A, Susi A, Avesani P (2012) A machine learning approach to software requirements prioritization. IEEE Trans Softw Eng 39(4):445\u2013461","journal-title":"IEEE Trans Softw Eng"},{"key":"395_CR47","doi-asserted-by":"crossref","unstructured":"Rahimi M, Guo JLC, Kokaly S, Chechik M (2019) Toward requirements specification for machine-learned components. In: 2019 IEEE 27th international requirements engineering conference workshops (REW). IEEE, pp 241\u2013244","DOI":"10.1109\/REW.2019.00049"},{"key":"395_CR48","unstructured":"Ralph P, bin Ali N, Baltes S, Bianculli D, Diaz J, Dittrich Y, Ernst N, Felderer M, Feldt R, Filieri A, de\u00a0Fran\u00e7a BBN, Furia CA, Gay G, Gold N, Graziotin D, He P, Hoda R, Juristo N, Kitchenham BA, Lenarduzzi V, Mart\u00ednez J, Melegati J, M\u00e9ndez D, Menzies T, Molleri J, Pfahl D, Robbes R, Russo D, Saarim\u00e4ki N, Sarro F, Taibi D, Siegmund J, Spinellis D, Staron M, Stol K-J, Storey M-A, Taibi D, Tamburri DA, Torchiano M, Treude C, Turhan B, Wang X, Vegas S (2021) ACM SIGSOFT empirical standards. CoRR. arXiv:2010.03525"},{"key":"395_CR49","doi-asserted-by":"crossref","unstructured":"Regnell B, H\u00f6st M, Berntsson\u00a0Svensson R (2007) A quality performance model for cost-benefit analysis of non-functional requirements applied to the mobile handset domain. In: International working conference on requirements engineering: foundation for software quality. Springer, Berlin, pp 277\u2013291","DOI":"10.1007\/978-3-540-73031-6_20"},{"issue":"2","key":"395_CR50","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1007\/s10664-008-9102-8","volume":"14","author":"P Runeson","year":"2009","unstructured":"Runeson P, H\u00f6st M (2009) Guidelines for conducting and reporting case study research in software engineering. Empir Softw Eng 14(2):131\u2013164","journal-title":"Empir Softw Eng"},{"key":"395_CR51","doi-asserted-by":"crossref","unstructured":"Sachdeva V, Chung L (2017) Handling non-functional requirements for big data and IoT projects in scrum. In: 2017 7th international conference on cloud computing, data science & engineering-confluence. IEEE, pp 216\u2013221","DOI":"10.1109\/CONFLUENCE.2017.7943152"},{"key":"395_CR52","first-page":"2503","volume":"28","author":"D Sculley","year":"2015","unstructured":"Sculley D, Holt G, Golovin D, Davydov E, Phillips T, Ebner D, Chaudhary V, Young M, Crespo J-F, Dennison D (2015) Hidden technical debt in machine learning systems. Adv Neural Inf Process Syst 28:2503\u20132511","journal-title":"Adv Neural Inf Process Syst"},{"key":"395_CR53","doi-asserted-by":"crossref","unstructured":"Siebert J, Joeckel L, Heidrich J, Nakamichi K, Ohashi K, Namba I, Yamamoto R, Aoyama M (2020) Towards guidelines for assessing qualities of machine learning systems. In: International conference on the quality of information and communications technology, pp 17\u201331. Springer, Berlin","DOI":"10.1007\/978-3-030-58793-2_2"},{"issue":"34","key":"395_CR54","first-page":"2008","volume":"32","author":"A Smola","year":"2008","unstructured":"Smola A, Vishwanathan SVN (2008) Introduction to machine learning. Cambridge University, UK 32(34):2008","journal-title":"Cambridge University, UK"},{"key":"395_CR55","doi-asserted-by":"crossref","unstructured":"Villamizar H, Escovedo T, Kalinowski M (2021) Requirements engineering for machine learning: a systematic mapping study. In: 2021 47th Euromicro conference on software engineering and advanced applications (SEAA). IEEE, pp 29\u201336","DOI":"10.1109\/SEAA53835.2021.00013"},{"key":"395_CR56","doi-asserted-by":"crossref","unstructured":"Vogelsang A, Borg M (2019) Requirements engineering for machine learning: perspectives from data scientists. In: 2019 IEEE 27th international requirements engineering conference workshops (REW). IEEE, pp 245\u2013251","DOI":"10.1109\/REW.2019.00050"},{"key":"395_CR57","unstructured":"Washizaki H, Khomh F, Gu\u00e9h\u00e9neuc Y-G, Takeuchi H, Okuda S, Natori N, Shioura N (2020) Software engineering patterns for machine learning applications (sep4mla) part 2. In: Proceedings of the 27th conference on pattern languages of programs, pp 1\u201310"},{"key":"395_CR58","doi-asserted-by":"crossref","unstructured":"Winkler J, Vogelsang A (2016) Automatic classification of requirements based on convolutional neural networks. In: 2016 IEEE 24th international requirements engineering conference workshops (REW). IEEE, pp 39\u201345","DOI":"10.1109\/REW.2016.021"}],"container-title":["Requirements Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00766-022-00395-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00766-022-00395-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00766-022-00395-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,10]],"date-time":"2023-05-10T19:38:59Z","timestamp":1683747539000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00766-022-00395-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,7]]},"references-count":58,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2023,6]]}},"alternative-id":["395"],"URL":"https:\/\/doi.org\/10.1007\/s00766-022-00395-3","relation":{},"ISSN":["0947-3602","1432-010X"],"issn-type":[{"value":"0947-3602","type":"print"},{"value":"1432-010X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,7]]},"assertion":[{"value":"24 May 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 November 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 January 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This work is supported by a Swedish Research Council (VR) Project: Non-Functional Requirements for Machine Learning: Facilitating Continuous Quality Awareness (iNFoRM).","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}