{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,26]],"date-time":"2025-07-26T09:17:12Z","timestamp":1753521432301,"version":"3.37.3"},"reference-count":66,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,10,18]],"date-time":"2024-10-18T00:00:00Z","timestamp":1729209600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,10,18]],"date-time":"2024-10-18T00:00:00Z","timestamp":1729209600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100005416","name":"Norges Forskningsr\u00e5d","doi-asserted-by":"publisher","award":["285528"],"award-info":[{"award-number":["285528"]}],"id":[{"id":"10.13039\/501100005416","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Empir Software Eng"],"published-print":{"date-parts":[[2025,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>Traditionally, software development teams in many industries have used copies of production databases or their masked, anonymized, or obfuscated versions for testing. However, privacy protection regulations, for example, the General Data Protection Regulation (GDPR), prohibit such practices. In such a situation, there is often a need to generate production-like test data, i.e., test data that is statistically representative of the production data and conforms to the domain\u2019s constraints. In this paper, we address this need by presenting a novel approach for generating production-like test data using deep learning techniques and studying the practical effectiveness of our proposed approach in industrial settings. We frame the problem of generating production-like test data as a Language Modeling problem. We then propose a general solution for test data generation and a framework for evaluating and comparing language models based on training effectiveness and the representativeness and validity of the generated data. To evaluate the practical effectiveness of our solution, we apply it to a case study: the Norwegian National Population Registry (NPR). Within the context of NPR, we experiment with three of the most successful Deep Learning algorithms for Language Modeling, namely Recurrent Neural Networks (RNN), Variational Autoencoders, and Generative Adversarial Networks (GANs). We furthermore evaluate and compare the effectiveness of these algorithms quantitatively, using the proposed evaluation framework. The results from our case study show that our approach can generate highly complex data that is statistically representative of the production data and conforms to the business rules of the domain. Moreover, test data generated with RNN, which outperforms the other two algorithms, are syntactically and semantically valid in more than 97% of the cases and are highly representative of the real NPR data. The practical applicability of our approach is evident from the fact that our approach was fully deployed in a test environment at the NPR, generating on-the-fly and scalable amounts of production-like test data that are used in the integration testing between NPR and its data consumers.<\/jats:p>","DOI":"10.1007\/s10664-024-10541-w","type":"journal-article","created":{"date-parts":[[2024,10,18]],"date-time":"2024-10-18T11:03:40Z","timestamp":1729249420000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Application of deep learning models to generate rich, dynamic and production-like test data"],"prefix":"10.1007","volume":"30","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0727-0092","authenticated-orcid":false,"given":"Chao","family":"Tan","sequence":"first","affiliation":[]},{"given":"Razieh","family":"Behjati","sequence":"additional","affiliation":[]},{"given":"Erik","family":"Arisholm","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,10,18]]},"reference":[{"key":"10541_CR1","unstructured":"char-rnn.pytorch. https:\/\/github.com\/spro\/char-rnn.pytorch. Accessed: 2019-08"},{"key":"10541_CR2","unstructured":"Latextgan-pytorch. https:\/\/github.com\/shreydesai\/latext-gan. Accessed: 2019-06"},{"key":"10541_CR3","unstructured":"Variational autoencoder (vae) in pytorch. https:\/\/wiseodd.github.io\/techblog\/2017\/01\/24\/vae-pytorch\/. Accessed: 2019-05"},{"issue":"10","key":"10541_CR4","doi-asserted-by":"publisher","first-page":"1376","DOI":"10.1109\/TSE.2013.17","volume":"39","author":"S Ali","year":"2013","unstructured":"Ali S, Iqbal MZ, Arcuri A, Briand LC (2013) Generating test data from ocl constraints with search techniques. IEEE Trans Software Eng 39(10):1376\u20131402","journal-title":"IEEE Trans Software Eng"},{"key":"10541_CR5","doi-asserted-by":"publisher","DOI":"10.1017\/S026357470426043X","author":"AM Andrew","year":"2003","unstructured":"Andrew AM (2003) Information theory, inference and learning algorithms. Cambridge University Press. https:\/\/doi.org\/10.1017\/S026357470426043X","journal-title":"Cambridge University Press"},{"key":"10541_CR6","unstructured":"Arjovsky M, Bottou L (2017) Towards principled methods for training generative adversarial networks. arXiv preprint arXiv:1701.04862"},{"key":"10541_CR7","doi-asserted-by":"publisher","unstructured":"Bayardo RJ, Agrawal R (2005) Data privacy through optimal k-anonymization. In: 21st International conference on data engineering (ICDE\u201905), pp. 217\u2013228. IEEE . https:\/\/doi.org\/10.1109\/ICDE.2005.42","DOI":"10.1109\/ICDE.2005.42"},{"key":"10541_CR8","doi-asserted-by":"publisher","unstructured":"Betts D, Dominguez J, Melnik G, Simonazzi F, Subramanian M (2013) Exploring CQRS and Event Sourcing: A Journey into High Scalability, Availability, and Maintainability with Windows Azure, 1st edn. Microsoft patterns & practices. https:\/\/doi.org\/10.5555\/2509680","DOI":"10.5555\/2509680"},{"key":"10541_CR9","doi-asserted-by":"publisher","unstructured":"Bowman SR, Vilnis L, Vinyals O, Dai AM, Jozefowicz R, Bengio S (2015) Generating sentences from a continuous space. arXiv preprint arXiv:1511.06349. https:\/\/doi.org\/10.18653\/v1\/k16-1002","DOI":"10.18653\/v1\/k16-1002"},{"key":"10541_CR10","doi-asserted-by":"crossref","unstructured":"\u010cegi\u0148 J, R\u00e1sto\u010dn\u1ef3 K (2020) Test data generation for mc\/dc criterion using reinforcement learning. In: 2020 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW), pp. 354\u2013357. IEEE","DOI":"10.1109\/ICSTW50294.2020.00063"},{"key":"10541_CR11","doi-asserted-by":"publisher","DOI":"10.1109\/tbdata.2017.2717439","author":"M Chen","year":"2017","unstructured":"Chen M, Shi X, Zhang Y, Wu D, Guizani M (2017) Deep features learning for medical image analysis with convolutional autoencoder neural network. IEEE Transactions on Big Data. https:\/\/doi.org\/10.1109\/tbdata.2017.2717439","journal-title":"IEEE Transactions on Big Data"},{"key":"10541_CR12","unstructured":"Cheon Y, Rubio-Medrano CE (2007) Random test data generation for java classes annotated with jml specifications"},{"key":"10541_CR13","doi-asserted-by":"publisher","unstructured":"Chulyadyo R, Leray P (2018) Using probabilistic relational models to generate synthetic spatial or non-spatial databases. In: 2018 12th International Conference on Research Challenges in Information Science (RCIS), pp. 1\u201312. IEEE. https:\/\/doi.org\/10.1109\/RCIS.2018.8406645","DOI":"10.1109\/RCIS.2018.8406645"},{"key":"10541_CR14","unstructured":"Chung J, Gulcehre C, Cho K, Bengio Y (2014) Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv e-prints arXiv:1412.3555"},{"key":"10541_CR15","unstructured":"Donahue D, Rumshisky A (2018) Adversarial text generation without reinforcement learning. arXiv e-prints arXiv:1810.06640"},{"key":"10541_CR16","doi-asserted-by":"publisher","unstructured":"Fuglede B, Topsoe F (2004) Jensen-shannon divergence and hilbert space embedding. In: International Symposium onInformation Theory, 2004. ISIT 2004. Proceedings., p.\u00a031. IEEE. https:\/\/doi.org\/10.1109\/ISIT.2004.1365067","DOI":"10.1109\/ISIT.2004.1365067"},{"key":"10541_CR17","doi-asserted-by":"publisher","unstructured":"Ghasedi\u00a0Dizaji K, Herandi A, Deng C, Cai W, Huang H (2017) Deep clustering via joint convolutional autoencoder embedding and relative entropy minimization. In: Proceedings of the IEEE international conference on computer vision, pp. 5736\u20135745. https:\/\/doi.org\/10.1109\/ICCV.2017.612","DOI":"10.1109\/ICCV.2017.612"},{"key":"10541_CR18","doi-asserted-by":"publisher","unstructured":"Ghosh A, Kulharia V, Mukerjee A, Namboodiri V, Bansal M (2017) Contextual rnn-gans for abstract reasoning diagram generation. In: Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence . https:\/\/doi.org\/10.5555\/3298239.3298441","DOI":"10.5555\/3298239.3298441"},{"key":"10541_CR19","doi-asserted-by":"crossref","unstructured":"Gois N, Porf\u00edrio P, Coelho A (2017) A multi-objective metaheuristic approach to search-based stress testing. In: 2017 IEEE International Conference on Computer and Information Technology (CIT), pp. 55\u201362. IEEE","DOI":"10.1109\/CIT.2017.19"},{"issue":"1","key":"10541_CR20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.2200\/S00762ED1V01Y201703HLT037","volume":"10","author":"Y Goldberg","year":"2017","unstructured":"Goldberg Y (2017) Neural network methods for natural language processing. Synthesis Lectures on Human Language Technologies 10(1):1\u2013309. https:\/\/doi.org\/10.2200\/S00762ED1V01Y201703HLT037","journal-title":"Synthesis Lectures on Human Language Technologies"},{"key":"10541_CR21","unstructured":"Goodfellow I (2016) Nips 2016 tutorial: Generative adversarial networks.arXiv e-prints arXiv:1701.00160"},{"key":"10541_CR22","unstructured":"Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial nets. In: Advances in neural information processing systems, pp. 2672\u20132680. http:\/\/papers.nips.cc\/paper\/5423-generative-adversarial-nets.pdf"},{"key":"10541_CR23","unstructured":"Graves A (2013) Generating sequences with recurrent neural networks. arXiv e-prints arXiv:1308.0850"},{"key":"10541_CR24","unstructured":"Gregor K, Danihelka I, Graves A, Rezende DJ, Wierstra D (2015) Draw: A recurrent neural network for image generation. arXiv e-prints arXiv:1502.04623"},{"key":"10541_CR25","unstructured":"Hardt M, Recht B, Singer Y (2016) Train faster, generalize better: Stability of stochastic gradient descent. In: International conference on machine learning, pp. 1225\u20131234. PMLR"},{"issue":"1","key":"10541_CR26","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1021\/ci0342472","volume":"44","author":"DM Hawkins","year":"2004","unstructured":"Hawkins DM (2004) The problem of overfitting. Journal of chemical information and computer sciences 44(1):1\u201312. https:\/\/doi.org\/10.1021\/ci0342472","journal-title":"Journal of chemical information and computer sciences"},{"key":"10541_CR27","doi-asserted-by":"publisher","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770\u2013778. https:\/\/doi.org\/10.1109\/CVPR.2016.90","DOI":"10.1109\/CVPR.2016.90"},{"key":"10541_CR28","doi-asserted-by":"crossref","unstructured":"Hoffman D, Wang HY, Chang M, Ly-Gagnon D (2009) Grammar based testing of html injection vulnerabilities in rss feeds. In: 2009 Testing: Academic and Industrial Conference-Practice and Research Techniques, pp. 105\u2013110. IEEE","DOI":"10.1109\/TAICPART.2009.34"},{"key":"10541_CR29","doi-asserted-by":"crossref","unstructured":"Hoffman DM, Ly-Gagnon D, Strooper P, Wang HY (2011) Grammar-based test generation with yougen. Software: Practice and Experience 41(4), 427\u2013447","DOI":"10.1002\/spe.1017"},{"key":"10541_CR30","doi-asserted-by":"crossref","unstructured":"Ji S, Chen Q, Zhang P (2019) Neural network based test case generation for data-flow oriented testing. In: 2019 IEEE International Conference On Artificial Intelligence Testing (AITest), pp. 35\u201336. IEEE","DOI":"10.1109\/AITest.2019.00-11"},{"key":"10541_CR31","unstructured":"Jurafsky D, University, J.H.M.S., of\u00a0Colorado\u00a0at Boulder), U (2019) Speech and Language Processing 3rd edition draft, oct 2019. https:\/\/web.stanford.edu\/~jurafsky\/slp3\/"},{"key":"10541_CR32","doi-asserted-by":"publisher","first-page":"142","DOI":"10.1016\/j.cageo.2019.02.003","volume":"126","author":"S Karimpouli","year":"2019","unstructured":"Karimpouli S, Tahmasebi P (2019) Segmentation of digital rock images using deep convolutional autoencoder networks. Computers & geosciences 126:142\u2013150. https:\/\/doi.org\/10.1016\/j.cageo.2019.02.003","journal-title":"Computers & geosciences"},{"key":"10541_CR33","unstructured":"Karpath A (2019) The unreasonable effectiveness of recurrent neural networks. http:\/\/karpathy.github.io\/2015\/05\/21\/rnn-effectiveness\/. Accessed: 2019-08-01"},{"key":"10541_CR34","unstructured":"Khari M, Kumar M, et\u00a0al. (2016) Analysis of software security testing using metaheuristic search technique. In: 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom), pp. 2147\u20132152. IEEE"},{"key":"10541_CR35","doi-asserted-by":"crossref","unstructured":"Kim J, Kwon M, Yoo S (2018) Generating test input with deep reinforcement learning. In: 2018 IEEE\/ACM 11th International Workshop on Search-Based Software Testing (SBST), pp. 51\u201358. IEEE","DOI":"10.1145\/3194718.3194720"},{"key":"10541_CR36","unstructured":"Kingma DP, Ba J (2014) Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980arXiv:1412.6980"},{"key":"10541_CR37","doi-asserted-by":"publisher","unstructured":"Ledig, C., Theis, L., Husz\u00e1r, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., Wang, Z., et\u00a0al. (2017) Photo-realistic single image super-resolution using a generative adversarial network. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 4681\u20134690. https:\/\/doi.org\/10.1109\/CVPR.2017.19","DOI":"10.1109\/CVPR.2017.19"},{"issue":"1","key":"10541_CR38","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s42400-018-0002-y","volume":"1","author":"J Li","year":"2018","unstructured":"Li J, Zhao B, Zhang C (2018) Fuzzing: a survey. Cybersecurity 1(1):1\u201313","journal-title":"Cybersecurity"},{"key":"10541_CR39","doi-asserted-by":"crossref","unstructured":"Li N, Lei Y, Khan HR, Liu J, Guo Y (2016) Applying combinatorial test data generation to big data applications. In: 2016 31st IEEE\/ACM International Conference on Automated Software Engineering (ASE), pp. 637\u2013647. IEEE","DOI":"10.1145\/2970276.2970325"},{"key":"10541_CR40","doi-asserted-by":"crossref","unstructured":"McMinn P (2011) Search-based software testing: Past, present and future. In: 2011 IEEE Fourth International Conference on Software Testing, Verification and Validation Workshops, pp. 153\u2013163. IEEE","DOI":"10.1109\/ICSTW.2011.100"},{"key":"10541_CR41","doi-asserted-by":"crossref","unstructured":"Medsker L, Jain LC (1999) Recurrent Neural Networks: Design and Applications. CRC Press","DOI":"10.1201\/9781420049176"},{"key":"10541_CR42","doi-asserted-by":"publisher","DOI":"10.1201\/9781420049176","volume-title":"Recurrent neural networks: design and applications","author":"L Medsker","year":"1999","unstructured":"Medsker L, Jain LC (1999) Recurrent neural networks: design and applications. CRC Press"},{"key":"10541_CR43","doi-asserted-by":"publisher","unstructured":"Nie D, Trullo R, Lian J, Petitjean C, Ruan S, Wang Q, Shen D (2017) Medical image synthesis with context-aware generative adversarial networks. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. https:\/\/doi.org\/10.1007\/978-3-319-66179-7_48","DOI":"10.1007\/978-3-319-66179-7_48"},{"key":"10541_CR44","doi-asserted-by":"crossref","unstructured":"Padhye R, Lemieux C, Sen K, Papadakis M, Le\u00a0Traon Y (2019) Semantic fuzzing with zest. In: Proceedings of the 28th ACM SIGSOFT International Symposium on Software Testing and Analysis, pp. 329\u2013340","DOI":"10.1145\/3293882.3330576"},{"key":"10541_CR45","doi-asserted-by":"crossref","unstructured":"Patki N, Wedge R, Veeramachaneni, K (2016) The synthetic data vault. In: 2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA), pp. 399\u2013410. IEEE","DOI":"10.1109\/DSAA.2016.49"},{"issue":"5","key":"10541_CR46","doi-asserted-by":"publisher","first-page":"265","DOI":"10.1016\/0020-0190(89)90206-8","volume":"30","author":"V Rajan","year":"1989","unstructured":"Rajan V, Ghosh R, Gupta P (1989) An efficient parallel algorithm for random sampling. Inf Process Lett 30(5):265\u2013268","journal-title":"Inf Process Lett"},{"key":"10541_CR47","doi-asserted-by":"publisher","unstructured":"Rajeswar, S., Subramanian, S., Dutil, F., Pal, C., Courville, A (2017) Adversarial generation of natural language. In: Proceedings of the 2nd Workshop on Representation Learning for NLP, pp. 241\u2013251. Association for Computational Linguistics. https:\/\/doi.org\/10.18653\/v1\/w17-2629","DOI":"10.18653\/v1\/w17-2629"},{"key":"10541_CR48","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.trb.2016.04.007","volume":"90","author":"I Saadi","year":"2016","unstructured":"Saadi I, Mustafa A, Teller J, Farooq B, Cools M (2016) Hidden markov model-based population synthesis. Transportation Research Part B: Methodological 90:1\u201321","journal-title":"Transportation Research Part B: Methodological"},{"key":"10541_CR49","doi-asserted-by":"crossref","unstructured":"Salecker E, Glesner S (2012) Combinatorial interaction testing for test selection in grammar-based testing. In: 2012 IEEE Fifth International Conference on Software Testing, Verification and Validation, pp. 610\u2013619. IEEE","DOI":"10.1109\/ICST.2012.148"},{"issue":"10","key":"10541_CR50","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1109\/MC.2016.314","volume":"49","author":"DE Simos","year":"2016","unstructured":"Simos DE, Kuhn R, Voyiatzis AG, Kacker R (2016) Combinatorial methods in security testing. IEEE Comput 49(10):80\u201383","journal-title":"IEEE Comput"},{"key":"10541_CR51","doi-asserted-by":"crossref","unstructured":"Soltana G, Sabetzadeh M, Briand LC (2017) Synthetic data generation for statistical testing. In: 2017 32nd IEEE\/ACM International Conference on Automated Software Engineering (ASE), pp. 872\u2013882. IEEE","DOI":"10.1109\/ASE.2017.8115698"},{"key":"10541_CR52","doi-asserted-by":"publisher","DOI":"10.1007\/s10270-016-0542-0","author":"G Soltana","year":"2018","unstructured":"Soltana G, Sannier N, Sabetzadeh M, Briand LC (2018) Model-based simulation of legal policies: Framework, tool support, and validation. Software & Systems Modeling. https:\/\/doi.org\/10.1007\/s10270-016-0542-0","journal-title":"Software & Systems Modeling"},{"key":"10541_CR53","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1016\/j.trc.2015.10.010","volume":"61","author":"L Sun","year":"2015","unstructured":"Sun L, Erath A (2015) A bayesian network approach for population synthesis. Transportation Research Part C: Emerging Technologies 61:49\u201362","journal-title":"Transportation Research Part C: Emerging Technologies"},{"key":"10541_CR54","unstructured":"Sun W (2015) Stability of machine learning algorithms. Ph.D. thesis, Purdue University"},{"key":"10541_CR55","doi-asserted-by":"crossref","unstructured":"Sundermeyer M, Schl\u00fcter R, Ney H (2012) Lstm neural networks for language modeling. In: Thirteenth annual conference of the international speech communication association. https:\/\/www.isca-speech.org\/archive\/interspeech_2012\/i12_0194.html","DOI":"10.21437\/Interspeech.2012-65"},{"key":"10541_CR56","unstructured":"Sutskever I, Martens J, Hinton GE (2011) Generating text with recurrent neural networks. In: Proceedings of the 28th International Conference on Machine Learning (ICML-11), pp. 1017\u20131024. https:\/\/icml.cc\/2011\/papers\/524_icmlpaper.pdf"},{"key":"10541_CR57","unstructured":"Tan C, Behjati R, Arisholm E (2018) Technical report: A semi-structured interview on test data need in the integration testing with the norwegian national registration. Tech. rep., Simula Research Lab. https:\/\/www.simula.no\/publications\/technical-report-semi-structured-interview-test-data-need-integration-testing-norwegian"},{"key":"10541_CR58","doi-asserted-by":"publisher","unstructured":"Theis L, Shi W, Cunningham A, Husz\u00e1r F (2017) Lossy image compression with compressive autoencoders. arXiv preprint arXiv:1703.00395. https:\/\/doi.org\/10.17863\/CAM.51995","DOI":"10.17863\/CAM.51995"},{"key":"10541_CR59","doi-asserted-by":"publisher","unstructured":"Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y., Manzagol, P.A (2010). Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion. Journal of machine learning research 11(Dec), 3371\u20133408 https:\/\/doi.org\/10.5555\/1756006.1953039","DOI":"10.5555\/1756006.1953039"},{"key":"10541_CR60","doi-asserted-by":"publisher","unstructured":"Wang W, Huang Y, Wang Y, Wang L (2014) Generalized autoencoder: A neural network framework for dimensionality reduction. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp. 490\u2013497. https:\/\/doi.org\/10.1109\/CVPRW.2014.79","DOI":"10.1109\/CVPRW.2014.79"},{"key":"10541_CR61","doi-asserted-by":"publisher","unstructured":"Webster, R., Rabin, J., Simon, L., Jurie, F (2019) Detecting overfitting of deep generative networks via latent recovery. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 11273\u201311282. https:\/\/doi.org\/10.1109\/CVPR.2019.01153","DOI":"10.1109\/CVPR.2019.01153"},{"issue":"1\u20133","key":"10541_CR62","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1016\/0169-7439(87)80084-9","volume":"2","author":"S Wold","year":"1987","unstructured":"Wold S, Esbensen K, Geladi P (1987) Principal component analysis. Chemom Intell Lab Syst 2(1\u20133):37\u201352. https:\/\/doi.org\/10.1016\/0169-7439(87)80084-9","journal-title":"Chemom Intell Lab Syst"},{"key":"10541_CR63","doi-asserted-by":"crossref","unstructured":"Yano T, Martins E, de\u00a0Sousa FL (2011) A model-based approach for robustness test generation. In: 2011 Fifth Latin-American Symposium on Dependable Computing Workshops, pp. 33\u201334. IEEE","DOI":"10.1109\/LADCW.2011.16"},{"key":"10541_CR64","doi-asserted-by":"publisher","unstructured":"Yi X, Li R, Sun M (2017) Generating chinese classical poems with rnn encoder-decoder. In: Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data, pp. 211\u2013223. Springer. https:\/\/doi.org\/10.1007\/978-3-319-69005-6_18","DOI":"10.1007\/978-3-319-69005-6_18"},{"key":"10541_CR65","unstructured":"Zhao J, Xiong L, Jayashree PK, Li J, Zhao F, Wang Z, Pranata PS, Shen PS, Yan S, Feng J (2017) Dual-agent gans for photorealistic and identity preserving profile face synthesis. In: Advances in Neural Information Processing Systems, pp. 66\u201376. http:\/\/papers.nips.cc\/paper\/6612-dual-agent-gans-for-photorealistic-and-identity-preserving-profile-face-synthesis.pdf"},{"key":"10541_CR66","doi-asserted-by":"crossref","unstructured":"Zhou X, Zhao R, You F (2014) Efsm-based test data generation with multi-population genetic algorithm. In: 2014 IEEE 5th International Conference on Software Engineering and Service Science, pp. 925\u2013928. IEEE","DOI":"10.1109\/ICSESS.2014.6933716"}],"container-title":["Empirical Software Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10664-024-10541-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10664-024-10541-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10664-024-10541-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,23]],"date-time":"2025-02-23T01:26:42Z","timestamp":1740274002000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10664-024-10541-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,18]]},"references-count":66,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,1]]}},"alternative-id":["10541"],"URL":"https:\/\/doi.org\/10.1007\/s10664-024-10541-w","relation":{},"ISSN":["1382-3256","1573-7616"],"issn-type":[{"type":"print","value":"1382-3256"},{"type":"electronic","value":"1573-7616"}],"subject":[],"published":{"date-parts":[[2024,10,18]]},"assertion":[{"value":"27 August 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 October 2024","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"5"}}