{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,6]],"date-time":"2026-04-06T21:32:44Z","timestamp":1775511164810,"version":"3.50.1"},"publisher-location":"Cham","reference-count":10,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030353322","type":"print"},{"value":"9783030353339","type":"electronic"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-35333-9_14","type":"book-chapter","created":{"date-parts":[[2019,11,18]],"date-time":"2019-11-18T00:01:29Z","timestamp":1574035289000},"page":"195-202","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Empirical Analysis of Hidden Technical Debt Patterns in Machine Learning Software"],"prefix":"10.1007","author":[{"given":"Mohannad","family":"Alahdab","sequence":"first","affiliation":[]},{"given":"G\u00fcl","family":"\u00c7al\u0131kl\u0131","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,11,18]]},"reference":[{"key":"14_CR1","unstructured":"Sculley, D., et al.: Hidden technical debt in machine learning systems. In: Proceedings of NIPS 2015, pp. 2503\u20132511. MIT Press, Montreal (2015)"},{"issue":"4","key":"14_CR2","first-page":"13:1","volume":"6","author":"CA Gomez-Uribe","year":"2016","unstructured":"Gomez-Uribe, C.A., Hunt, N.: The netflix recommender system: algorithms, business value, and innovation. ACM Trans. Manag. Inf. Syst. 6(4), 13:1\u201313:19 (2016)","journal-title":"ACM Trans. Manag. Inf. Syst."},{"key":"14_CR3","doi-asserted-by":"crossref","unstructured":"Kenthapadi, K., Le, B., Venkataraman, G.: Personalized job recommendation system at LinkedIn: practical challenges and lessons learned. In: Proceedings of 11th ACM Conference on Recommender Systems, Como, Italy, pp. 346\u2013347 (2017)","DOI":"10.1145\/3109859.3109921"},{"key":"14_CR4","doi-asserted-by":"crossref","unstructured":"Hazelwood, K., et al.: Applied machine learning at facebook: a datacenter infrastructure perspective. In: IEEE International Symposium on High Performance Computer Architecture Proceedings, pp. 620\u2013629, Vienna, Austria (2018)","DOI":"10.1109\/HPCA.2018.00059"},{"key":"14_CR5","unstructured":"Martinez-Plumed, F., et al.: Accounting for the neglected dimensions of AI progress (2018). \nhttps:\/\/arxiv.org\/abs\/1806.00610"},{"key":"14_CR6","unstructured":"Agarwal, A., et al.: Making contextual decisions with low technical debt (2017). \nhttps:\/\/arxiv.org\/abs\/1806"},{"key":"14_CR7","doi-asserted-by":"crossref","unstructured":"Breck, E., Cai, S., Nielsen, E., Salib, M., Sculley, D.: The ML test score: a rubric for ML production readiness and technical debt reduction. In: BigData 2018, pp. 1123\u20131133. IEEE, Boston (2017)","DOI":"10.1109\/BigData.2017.8258038"},{"key":"14_CR8","doi-asserted-by":"crossref","unstructured":"Kim, J., Feldt, R., Yoo, S.: Guiding deep learning testing using surprise adequacy. In: ICSE 2019, pp. 303\u2013314. IEEE, Montreal (2019)","DOI":"10.1109\/ICSE.2019.00108"},{"key":"14_CR9","unstructured":"Balzer, P.: Prediction of car park occupancy. \nhttp:\/\/mechlab-engineering.de\/2015\/03\/vorhersage-derparkhausbelegung-mit-offenen-daten\/\n\n. Accessed May 2019"},{"key":"14_CR10","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4615-5689-3","volume-title":"Feature Selection for Knowledge Discovery and Data Mining","author":"H Liu","year":"1998","unstructured":"Liu, H., Motoda, H.: Feature Selection for Knowledge Discovery and Data Mining. Springer, New York (1998). \nhttps:\/\/doi.org\/10.1007\/978-1-4615-5689-3"}],"container-title":["Lecture Notes in Computer Science","Product-Focused Software Process Improvement"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-35333-9_14","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,11,18]],"date-time":"2019-11-18T00:14:12Z","timestamp":1574036052000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-35333-9_14"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030353322","9783030353339"],"references-count":10,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-35333-9_14","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"18 November 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PROFES","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Product-Focused Software Process Improvement","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Barcelona","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 November 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 November 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"profes2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/profes2019.upc.edu\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"65","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"24","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"11","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"37% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"The 11 short papers were selected from 30 submissions.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}