{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T03:23:16Z","timestamp":1742959396170,"version":"3.40.3"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030692438"},{"type":"electronic","value":"9783030692445"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","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":[[2021]]},"DOI":"10.1007\/978-3-030-69244-5_2","type":"book-chapter","created":{"date-parts":[[2021,2,20]],"date-time":"2021-02-20T16:36:12Z","timestamp":1613838972000},"page":"17-29","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Heterogeneous Software Effort Estimation via Cascaded Adversarial Auto-Encoder"],"prefix":"10.1007","author":[{"given":"Fumin","family":"Qi","sequence":"first","affiliation":[]},{"given":"Xiao-Yuan","family":"Jing","sequence":"additional","affiliation":[]},{"given":"Xiaoke","family":"Zhu","sequence":"additional","affiliation":[]},{"given":"Xiaodong","family":"Jia","sequence":"additional","affiliation":[]},{"given":"Li","family":"Cheng","sequence":"additional","affiliation":[]},{"given":"Yichuan","family":"Dong","sequence":"additional","affiliation":[]},{"given":"Ziseng","family":"Fang","sequence":"additional","affiliation":[]},{"given":"Fei","family":"Ma","sequence":"additional","affiliation":[]},{"given":"Shengzhong","family":"Feng","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,2,21]]},"reference":[{"issue":"1","key":"2_CR1","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1109\/TSE.2007.256943","volume":"33","author":"M Jorgensen","year":"2007","unstructured":"Jorgensen, M., Shepperd, M.: A systematic review of software development cost estimation studies. IEEE Trans. Softw. Eng. 33(1), 33\u201353 (2007). https:\/\/doi.org\/10.1109\/TSE.2007.256943","journal-title":"IEEE Trans. Softw. Eng."},{"issue":"4","key":"2_CR2","doi-asserted-by":"publisher","first-page":"e1925","DOI":"10.1002\/smr.1925","volume":"30","author":"A Idri","year":"2018","unstructured":"Idri, A., Abnane, I., Abran, A.: Evaluating Pred (p) and standardized accuracy criteria in software development effort estimation. J. Softw. Evol. Process 30(4), e1925 (2018). https:\/\/doi.org\/10.1002\/smr.1925","journal-title":"J. Softw. Evol. Process"},{"issue":"5","key":"2_CR3","doi-asserted-by":"publisher","first-page":"3153","DOI":"10.1007\/s10664-019-09686-w","volume":"24","author":"LL Minku","year":"2019","unstructured":"Minku, L.L.: A novel online supervised hyperparameter tuning procedure applied to cross-company software effort estimation. Empirical Softw. Eng. 24(5), 3153\u20133204 (2019). https:\/\/doi.org\/10.1007\/s10664-019-09686-w","journal-title":"Empirical Softw. Eng."},{"key":"2_CR4","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1016\/j.infsof.2017.07.015","volume":"92","author":"F Qi","year":"2017","unstructured":"Qi, F., Jing, X.-Y., Zhu, X., Xie, X., Xu, B., Ying, S.: Grid information services for distributed resource sharing. Inf. Softw. Technol. 92, 145\u2013157 (2017). https:\/\/doi.org\/10.1016\/j.infsof.2017.07.015","journal-title":"Inf. Softw. Technol."},{"key":"2_CR5","unstructured":"Boehm, B.W., Madachy, R., Steece, B.: Software cost estimation with Cocomo II with Cdrom, pp. 540\u2013541. Prentice Hall PTR (2000). book\/10.5555\/557000"},{"issue":"1","key":"2_CR6","doi-asserted-by":"publisher","first-page":"2","DOI":"10.1109\/32.4618","volume":"14","author":"CR Symons","year":"1998","unstructured":"Symons, C.R.: Function point analysis: difficulties and improvements. IEEE Trans. Software Eng. 14(1), 2\u201311 (1998). https:\/\/doi.org\/10.1109\/32.4618","journal-title":"IEEE Trans. Software Eng."},{"key":"2_CR7","doi-asserted-by":"publisher","unstructured":"Mohagheghi, P., Anda, B., Conradi, R.: Effort estimation of use cases for incremental large-scale software development. In: 27th International Conference on Software Engineering, New York, pp. 303\u2013311. IEEE (2005). https:\/\/doi.org\/10.1109\/ICSE.2005.1553573","DOI":"10.1109\/ICSE.2005.1553573"},{"issue":"12","key":"2_CR8","doi-asserted-by":"publisher","first-page":"e2114","DOI":"10.1002\/smr.1925","volume":"30","author":"A Idri","year":"2018","unstructured":"Idri, A., Abnane, I., Abran, A.: Support vector regression-based imputation in analogy-based software development effort estimation. J. Softw. Evol. Process 30(12), e2114 (2018). https:\/\/doi.org\/10.1002\/smr.1925","journal-title":"J. Softw. Evol. Process"},{"key":"2_CR9","doi-asserted-by":"publisher","first-page":"158","DOI":"10.1016\/j.swevo.2017.07.009","volume":"38","author":"TR Benala","year":"2018","unstructured":"Benala, T.R., Mall, R.: DABE: differential evolution in analogy-based software development effort estimation. Swarm Evol. Comput. 38, 158\u2013172 (2018). https:\/\/doi.org\/10.1016\/j.swevo.2017.07.009","journal-title":"Swarm Evol. Comput."},{"key":"2_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.jss.2016.11.029","volume":"125","author":"R Silhavy","year":"2017","unstructured":"Silhavy, R., Silhavy, P., Prokopova, Z.: Analysis and selection of a regression model for the use case points method using a stepwise approach. J. Syst. Softw. 125, 1\u201314 (2017). https:\/\/doi.org\/10.1016\/j.jss.2016.11.029","journal-title":"J. Syst. Softw."},{"issue":"8","key":"2_CR11","doi-asserted-by":"publisher","first-page":"356","DOI":"10.17706\/jsw.14.8.356-369","volume":"14","author":"A Altaleb","year":"2019","unstructured":"Altaleb, A., Gravell, A.: An empirical investigation of effort estimation in mobile apps using agile development process. J. Softw. 14(8), 356\u2013369 (2019). https:\/\/doi.org\/10.17706\/jsw.14.8.356-369","journal-title":"J. Softw."},{"issue":"1","key":"2_CR12","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1016\/j.infsof.2011.09.002","volume":"54","author":"J Wen","year":"2012","unstructured":"Wen, J., Li, S., Lin, Z., Hu, Y., Huang, C.: Systematic literature review of machine learning based software development effort estimation models. Inf. Softw. Technol. 54(1), 41\u201359 (2012). https:\/\/doi.org\/10.1016\/j.infsof.2011.09.002","journal-title":"Inf. Softw. Technol."},{"issue":"2","key":"2_CR13","doi-asserted-by":"publisher","first-page":"425","DOI":"10.1109\/tse.2011.27","volume":"38","author":"E Kocaguneli","year":"2012","unstructured":"Kocaguneli, E., Menzies, T., Bener, A., Keung, J.W.: Exploiting the essential assumptions of analogy-based effort estimation. IEEE Trans. Software Eng. 38(2), 425\u2013438 (2012). https:\/\/doi.org\/10.1109\/tse.2011.27","journal-title":"IEEE Trans. Software Eng."},{"issue":"15","key":"2_CR14","doi-asserted-by":"publisher","first-page":"911","DOI":"10.1016\/s0950-5849(02)00128-3","volume":"44","author":"A Heiat","year":"2002","unstructured":"Heiat, A.: Comparison of artificial neural network and regression models for estimating software development effort. Inf. Softw. Technol. 44(15), 911\u2013922 (2002). https:\/\/doi.org\/10.1016\/s0950-5849(02)00128-3","journal-title":"Inf. Softw. Technol."},{"issue":"3","key":"2_CR15","doi-asserted-by":"publisher","first-page":"253","DOI":"10.1016\/s0164-1212(03)00066-9","volume":"68","author":"M J\u00f8rgensen","year":"2003","unstructured":"J\u00f8rgensen, M., Indahl, U., Sj\u00f8berg, D.: Software effort estimation by analogy and \u201cregression toward the mean\u201d. J. Syst. Softw. 68(3), 253\u2013256 (2003). https:\/\/doi.org\/10.1016\/s0164-1212(03)00066-9","journal-title":"J. Syst. Softw."},{"issue":"3","key":"2_CR16","doi-asserted-by":"publisher","first-page":"12:1","DOI":"10.1145\/3234940","volume":"27","author":"F Sarro","year":"2018","unstructured":"Sarro, F., Petrozziello, A.: Linear programming as a baseline for software effort estimation. ACM Trans. Softw. Eng. Methodol. 27(3), 12:1\u201312:28 (2018). https:\/\/doi.org\/10.1145\/3234940","journal-title":"ACM Trans. Softw. Eng. Methodol."},{"issue":"3","key":"2_CR17","doi-asserted-by":"publisher","first-page":"813","DOI":"10.1007\/s10664-014-9300-5","volume":"20","author":"E Kocaguneli","year":"2015","unstructured":"Kocaguneli, E., Menzies, T., Mendes, E.: Transfer learning in effort estimation. Empirical Softw. Eng. 20(3), 813\u2013843 (2015). https:\/\/doi.org\/10.1007\/s10664-014-9300-5","journal-title":"Empirical Softw. Eng."},{"key":"2_CR18","doi-asserted-by":"publisher","unstructured":"Minku, L.L., Yao, X.: How to make best use of cross-company data in software effort estimation? In: 36th International Conference on Software Engineering, Hyderabad, pp. 446\u2013456. IEEE (2014). https:\/\/doi.org\/10.1145\/2568225.2568228","DOI":"10.1145\/2568225.2568228"},{"key":"2_CR19","doi-asserted-by":"publisher","unstructured":"Tong, S., He, Q., Chen, Y., Yang, Y., Shen, B.: Heterogeneous cross-company effort estimation through transfer learning. In: 23rd Asia-Pacific Software Engineering Conference, Hamilton, pp. 169\u2013176. IEEE (2016). https:\/\/doi.org\/10.1109\/APSEC.2016.033","DOI":"10.1109\/APSEC.2016.033"},{"key":"2_CR20","unstructured":"Makhzani, A., Shlens, J., Jaitly, N., Goodfellow, I., Frey, B.: Adversarial autoencoders (2015). https:\/\/arxiv.org\/abs\/1511.05644"},{"issue":"8","key":"2_CR21","doi-asserted-by":"publisher","first-page":"1105","DOI":"10.1049\/iet-cvi.2018.5243","volume":"12","author":"A Creswell","year":"2018","unstructured":"Creswell, A., Pouplin, A., Bharath, A.A.: Denoising adversarial autoencoders: classifying skin lesions using limited labelled training data. IET Comput. Vision 12(8), 1105\u20131111 (2018). https:\/\/doi.org\/10.1049\/iet-cvi.2018.5243","journal-title":"IET Comput. Vision"},{"issue":"9","key":"2_CR22","doi-asserted-by":"publisher","first-page":"874","DOI":"10.1109\/TSE.2017.2720603","volume":"44","author":"J Nam","year":"2017","unstructured":"Nam, J., Fu, W., Kim, S., Menzies, T., Tan, L.: Heterogeneous defect prediction. IEEE Trans. Softw. Eng. 44(9), 874\u2013896 (2017). https:\/\/doi.org\/10.1109\/TSE.2017.2720603","journal-title":"IEEE Trans. Softw. Eng."}],"container-title":["Lecture Notes in Computer Science","Parallel and Distributed Computing, Applications and Technologies"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-69244-5_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,2,20]],"date-time":"2021-02-20T16:36:16Z","timestamp":1613838976000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-69244-5_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030692438","9783030692445"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-69244-5_2","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"21 February 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PDCAT","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Parallel and Distributed Computing: Applications and Technologies","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Shenzhen","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 December 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 December 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"pdcat2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/hpcc.siat.ac.cn\/meeting\/pdcat-paap2020\/index.html","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":"OCS","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"109","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":"34","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":"0","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":"31% - 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":"6","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)"}}]}}