{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T05:24:39Z","timestamp":1773725079495,"version":"3.50.1"},"reference-count":28,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T00:00:00Z","timestamp":1771545600000},"content-version":"vor","delay-in-days":19,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2026,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>With increasing complexity and importance in the society of software systems, testing software products becomes increasingly challenging and one of the problems that appears is determining the correct output given an input, named the test oracle problem. The aim of the paper is two fold: (1) to replicate the findings in a previous work regarding the use of various neural network models for the test oracle problem, and (2) to investigate further aspects regarding hyperparameter optimization by using the L4 Taguchi approach. Three datasets were used, two from previous studies and one created. In the execution of the experiment it is also simulated the real regression testing process by using mutation datasets. The results from the replication experiments show similar results to the ones from the original research, on the Triangle dataset, the ANN (Artificial Neural Networks) has a 0.12 MARE (Mean Absolute Relative Error) score while RBF (Radial Basis Function) 0.23; however, for both Bank Credit and Heart Risk datasets the RBF obtained the best MARE results, 0.09 and 0.12. The Taguchi L4 method does not offer a single optimal solution for a model for all datasets, but we can observe some trends in the results. Across all experiments, for the epochs parameters it seems that the best results are for 100 for all datasets and for the two models, except for the triangle dataset and ANN model. Regarding the hidden layers, best results are for the 50 nodes in the case of the ANN and 350 nodes in case of the RBF.<\/jats:p>","DOI":"10.1007\/s00521-026-11909-3","type":"journal-article","created":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T02:20:22Z","timestamp":1771554022000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Neural networks-based automated test oracles"],"prefix":"10.1007","volume":"38","author":[{"given":"Mihai-Aron","family":"Vulcan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andreea","family":"Vescan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,2,20]]},"reference":[{"key":"11909_CR1","unstructured":"ACM (2024) Association for computing machinery. https:\/\/www.acm.org\/, online; accessed 25 May 2024"},{"key":"11909_CR2","doi-asserted-by":"publisher","first-page":"1183","DOI":"10.1109\/TSMCA.2012.2183590","volume":"42","author":"D Agarwal","year":"2012","unstructured":"Agarwal D, Tamir D, Last M et al (2012) A comparative study of artificial neural networks and info-fuzzy networks as automated oracles in software testing Systems Man and Cybernetics Part A Systems and Humans. IEEE Trans 42:1183\u20131193. https:\/\/doi.org\/10.1109\/TSMCA.2012.2183590","journal-title":"IEEE Trans"},{"key":"11909_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/986710.986725","volume":"29","author":"K Aggarwal","year":"2004","unstructured":"Aggarwal K, Singh Y, Kaur A et al (2004) A neural net based approach to test oracle. ACM SIGSOFT Softw Eng Notes 29:1\u20136. https:\/\/doi.org\/10.1145\/986710.986725","journal-title":"ACM SIGSOFT Softw Eng Notes"},{"key":"11909_CR4","doi-asserted-by":"publisher","unstructured":"Alonso JC, Segura S, Ruiz-Cort\u00e9s A (2023) Agora: Automated generation of test oracles for rest apis. In: Proceedings of the 32nd ACM SIGSOFT International Symposium on Software Testing and Analysis. Association for Computing Machinery, New York, NY, USA, ISSTA 2023, p 1018\u20131030, https:\/\/doi.org\/10.1145\/3597926.3598114, https:\/\/doi.org\/10.1145\/3597926.3598114","DOI":"10.1145\/3597926.3598114"},{"key":"11909_CR5","doi-asserted-by":"crossref","unstructured":"Ammann P, Offutt J (2016) Introduction to software testing, 2nd edn. Cambridge University Press","DOI":"10.1017\/9781316771273"},{"key":"11909_CR6","doi-asserted-by":"publisher","unstructured":"Anonymized D  Neural networks-based automated test oracles. https:\/\/doi.org\/10.6084\/m9.figshare.26124871. Accessed Jun 2024","DOI":"10.6084\/m9.figshare.26124871"},{"key":"11909_CR7","unstructured":"Anonymous N (2024) Neural networks based automated test oracles. Master\u2019s thesis, Anonymous University, Dept of Computer Science, Software Engineering Master Section"},{"key":"11909_CR8","doi-asserted-by":"publisher","unstructured":"Baral K, Johnson J, Mahmud J, et al (2024) Automating gui-based test oracles for mobile apps. In: Proc 21st Intl Conf Mining Softw Repositories. Association for Computing Machinery, New York, NY, USA, MSR \u201924, p 309\u2013321, https:\/\/doi.org\/10.1145\/3643991.3644930,","DOI":"10.1145\/3643991.3644930"},{"issue":"1","key":"11909_CR9","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1109\/MS.2005.6","volume":"22","author":"T Dyba","year":"2005","unstructured":"Dyba T, Kitchenham BA, Jorgensen M (2005) Evidence-based software engineering for practitioners. IEEE Softw 22(1):58\u201365","journal-title":"IEEE Softw"},{"key":"11909_CR10","doi-asserted-by":"publisher","unstructured":"Gartziandia A, Arrieta A, Ayerdi J et al (2022) Machine learning-based test oracles for performance testing of cyber-physical systems: an industrial case study on elevators dispatching algorithms. J Softw Evolution Proc 34. https:\/\/doi.org\/10.1002\/smr.2465","DOI":"10.1002\/smr.2465"},{"key":"11909_CR11","doi-asserted-by":"publisher","unstructured":"Haran M, Karr A, Orso A, et al (2005) Applying classification techniques to remotely-collected program execution data. pp 146\u2013155, https:\/\/doi.org\/10.1145\/1095430.1081732","DOI":"10.1145\/1095430.1081732"},{"key":"11909_CR12","doi-asserted-by":"publisher","unstructured":"He W, Di P, Ming M, et al (2024) Finding and understanding defects in static analyzers by constructing automated oracles. Proc ACM Softw Eng 1(FSE). https:\/\/doi.org\/10.1145\/3660781,","DOI":"10.1145\/3660781"},{"key":"11909_CR13","doi-asserted-by":"publisher","unstructured":"Hossain SB (2024) Ensuring critical properties of test oracles for effective bug detection. In: Proceedings of the 2024 IEEE\/ACM 46th international conference on software engineering: companion proceedings. Association for computing machinery, New York, NY, USA, ICSE-Companion \u201924, p 176\u2013180, https:\/\/doi.org\/10.1145\/3639478.3639791,","DOI":"10.1145\/3639478.3639791"},{"key":"11909_CR14","doi-asserted-by":"publisher","unstructured":"Ibrahimzada AR, Varli Y, Tekinoglu D, et al (2022a) Perfect is the enemy of test oracle. In: proceedings of the 30th ACM joint european software engineering conference and symposium on the foundations of software engineering. Association for Computing Machinery, New York, NY, USA, ESEC\/FSE 2022, p 70\u201381, https:\/\/doi.org\/10.1145\/3540250.3549086,","DOI":"10.1145\/3540250.3549086"},{"key":"11909_CR15","doi-asserted-by":"publisher","unstructured":"Ibrahimzada AR, Varli Y, Tekinoglu D, et al (2022b) Perfect is the enemy of test oracle. In: proceedings of the 30th ACM joint european software engineering conference and symposium on the foundations of software engineering. Association for computing machinery, New York, NY, USA, ESEC\/FSE 2022, p 70\u201381, https:\/\/doi.org\/10.1145\/3540250.3549086,","DOI":"10.1145\/3540250.3549086"},{"key":"11909_CR16","unstructured":"IEEE (2024) Ieee xplore. https:\/\/ieeexplore.ieee.org\/Xplore\/home.jsp, online; accessed 25 May 2024"},{"key":"11909_CR17","unstructured":"Kitchenham B, Charters S (2007) Guidelines for performing systematic literature reviews in software engineering 2"},{"issue":"5","key":"11909_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3715107","volume":"34","author":"F Molina","year":"2025","unstructured":"Molina F, Gorla A, d\u2019Amorim M (2025) Test oracle automation in the era of llms. ACM Trans Softw Eng Methodol 34(5):1\u201324. https:\/\/doi.org\/10.1145\/3715107","journal-title":"ACM Trans Softw Eng Methodol"},{"key":"11909_CR19","doi-asserted-by":"publisher","unstructured":"Molinelli D, Martin-Lopez A, Zackrone E, et al (2025) Tratto: A neuro-symbolic approach to deriving axiomatic test oracles. Proc ACM Softw Eng 2(ISSTA). https:\/\/doi.org\/10.1145\/3728960,","DOI":"10.1145\/3728960"},{"key":"11909_CR20","volume-title":"A primer on the taguchi method","author":"RK Roy","year":"2010","unstructured":"Roy RK (2010) A primer on the taguchi method, 2nd edn. Society of Manufacturing Engineers, USA","edition":"2"},{"key":"11909_CR21","unstructured":"Russo A (2024) Pytorch rbf layer - radial basis function layer. https:\/\/github.com\/rssalessio\/PytorchRBFLayer\/tree\/main, online; accessed 25 May 2024"},{"key":"11909_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2020976.2020992","volume":"36","author":"O Sangwan","year":"2011","unstructured":"Sangwan O, Bhatia P, Singh Y (2011) Radial basis function neural network based approach to test oracle. ACM SIGSOFT Softw Eng Notes 36:1\u20135. https:\/\/doi.org\/10.1145\/2020976.2020992","journal-title":"ACM SIGSOFT Softw Eng Notes"},{"key":"11909_CR23","doi-asserted-by":"publisher","unstructured":"Shahamiri SR, Wan Kadir WMN, Ibrahim S (2010a) An automated oracle approach to test decision-making structures. pp 30 \u2013 34, https:\/\/doi.org\/10.1109\/ICCSIT.2010.5563989","DOI":"10.1109\/ICCSIT.2010.5563989"},{"key":"11909_CR24","doi-asserted-by":"publisher","unstructured":"Shahamiri SR, Wan Kadir WMN, Ibrahim S (2010b) A single-network ann-based oracle to verify logical software modules. pp V2\u2013272, https:\/\/doi.org\/10.1109\/ICSTE.2010.5608808","DOI":"10.1109\/ICSTE.2010.5608808"},{"key":"11909_CR25","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1016\/j.infsof.2018.01.006","volume":"99","author":"M Shepperd","year":"2018","unstructured":"Shepperd M, Ajienka N, Counsell S (2018) The role and value of replication in empirical software engineering results. Inf Softw Technol 99:120\u2013132. https:\/\/doi.org\/10.1016\/j.infsof.2018.01.006","journal-title":"Inf Softw Technol"},{"key":"11909_CR26","doi-asserted-by":"publisher","unstructured":"<error l=\u201c354\u201d c=\u201cbad csname\u201d \/>]Tsimpourlas2021 Tsimpourlas F, Rajan A, Allamanis M (2021) Supervised learning over test executions as a test oracle. pp 1521\u20131531, https:\/\/doi.org\/10.1145\/3412841.3442027","DOI":"10.1145\/3412841.3442027"},{"key":"11909_CR27","unstructured":"Yin RK (2008) Case Study Research : Design and Methods. SAGE"},{"key":"11909_CR28","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/ACCESS.2017.2758790","volume":"5","author":"P Zhang","year":"2017","unstructured":"Zhang P, Zhou X, Pelliccione P et al (2017) Rbf-mlmr: a multi-label metamorphic relation prediction approach using rbf neural network. IEEE Access 5:1\u20131. https:\/\/doi.org\/10.1109\/ACCESS.2017.2758790","journal-title":"IEEE Access"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-026-11909-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-026-11909-3","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-026-11909-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T03:43:59Z","timestamp":1773719039000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-026-11909-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2]]},"references-count":28,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2026,2]]}},"alternative-id":["11909"],"URL":"https:\/\/doi.org\/10.1007\/s00521-026-11909-3","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2]]},"assertion":[{"value":"18 March 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 January 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 February 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that there is no Conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Both authors consent the publication of this work.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"Not applicable.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Materials availability"}},{"value":"Not applicable.","order":6,"name":"Ethics","group":{"name":"EthicsHeading","label":"Code availability"}}],"article-number":"66"}}