The challenge of generating and evolving real-life like synthetic test data without accessing real-world raw data: a systematic review

Background High-level system testing of applications that use data from e-Government services as input requires test data that is real-life-like but where the privacy of personal information is guaranteed. Applications with such strong requirement include information exchange between countries, medi...

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Detalles Bibliográficos
Autores: Tammisto, Maj-Annika, Shah, Faiz Ali, Rodríguez García, Daniel|||0000-0002-2887-0185, Pfahl, Dietmar
Tipo de recurso: artículo
Fecha de publicación:2025
País:España
Institución:Universidad de Alcalá (UAH)
Repositorio:e_Buah Biblioteca Digital Universidad de Alcalá
Idioma:inglés
OAI Identifier:oai:ebuah.uah.es:10017/67590
Acceso en línea:http://hdl.handle.net/10017/67590
https://dx.doi.org/10.1111/exsy.70164
Access Level:acceso abierto
Palabra clave:Artificial data
Data evolution
Data synthesis
Synthetic data generation
Synthetic test data
Synthetically generated data
Informática
Computer science
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spelling The challenge of generating and evolving real-life like synthetic test data without accessing real-world raw data: a systematic reviewTammisto, Maj-AnnikaShah, Faiz AliRodríguez García, Daniel|||0000-0002-2887-0185Pfahl, DietmarArtificial dataData evolutionData synthesisSynthetic data generationSynthetic test dataSynthetically generated dataInformáticaComputer scienceBackground High-level system testing of applications that use data from e-Government services as input requires test data that is real-life-like but where the privacy of personal information is guaranteed. Applications with such strong requirement include information exchange between countries, medicine, banking, and so on. This review aims to synthesise the current state-of-the-practice in this domain. Objectives The objective of this Systematic Review is to identify existing approaches for creating and evolving synthetic test data without using real-life raw data. Methods We followed well-known methodologies for conducting systematic literature reviews, including the ones from Kitchenham and PRISMA as well as guidelines for analysing the limitations of our review and its threats to validity. Results A variety of methods and tools exist for creating privacy-preserving test data. Our search found 1013 publications in IEEE Xplore, ACM Digital Library, and SCOPUS. We extracted data from 75 of those publications and identified 37 approaches that answer our research question partly. A common prerequisite for using these methods and tools is direct access to real-life data for data anonymization or synthetic test data generation. Nine existing synthetic test data generation approaches were identified that were closest to answering our research question. Nevertheless, further work would be needed to add the ability to evolve synthetic test data to the existing approaches. Conclusions None of the publications covered our requirements completely, only partially. Synthetic test data evolution is a field that has not received much attention from researchers but needs to be explored in Digital Government Solutions, especially since new legal regulations are being put in force in many countries.Junta de Comunidades de Castilla-La ManchaJohn Wiley & Sons20252025-11-08journal articlehttp://purl.org/coar/resource_type/c_6501NAhttp://purl.org/coar/version/c_be7fb7dd8ff6fe43info:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10017/67590https://dx.doi.org/10.1111/exsy.70164reponame:e_Buah Biblioteca Digital Universidad de Alcaláinstname:Universidad de Alcalá (UAH)InglésengJunta de Comunidades de Castilla-La Mancha http://dx.doi.org/10.13039/501100011698 Not available SBPLY%2F24%2F180225%2F000143open accesshttp://purl.org/coar/access_right/c_abf2Attribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:ebuah.uah.es:10017/675902026-06-18T11:13:07Z
dc.title.none.fl_str_mv The challenge of generating and evolving real-life like synthetic test data without accessing real-world raw data: a systematic review
title The challenge of generating and evolving real-life like synthetic test data without accessing real-world raw data: a systematic review
spellingShingle The challenge of generating and evolving real-life like synthetic test data without accessing real-world raw data: a systematic review
Tammisto, Maj-Annika
Artificial data
Data evolution
Data synthesis
Synthetic data generation
Synthetic test data
Synthetically generated data
Informática
Computer science
title_short The challenge of generating and evolving real-life like synthetic test data without accessing real-world raw data: a systematic review
title_full The challenge of generating and evolving real-life like synthetic test data without accessing real-world raw data: a systematic review
title_fullStr The challenge of generating and evolving real-life like synthetic test data without accessing real-world raw data: a systematic review
title_full_unstemmed The challenge of generating and evolving real-life like synthetic test data without accessing real-world raw data: a systematic review
title_sort The challenge of generating and evolving real-life like synthetic test data without accessing real-world raw data: a systematic review
dc.creator.none.fl_str_mv Tammisto, Maj-Annika
Shah, Faiz Ali
Rodríguez García, Daniel|||0000-0002-2887-0185
Pfahl, Dietmar
author Tammisto, Maj-Annika
author_facet Tammisto, Maj-Annika
Shah, Faiz Ali
Rodríguez García, Daniel|||0000-0002-2887-0185
Pfahl, Dietmar
author_role author
author2 Shah, Faiz Ali
Rodríguez García, Daniel|||0000-0002-2887-0185
Pfahl, Dietmar
author2_role author
author
author
dc.subject.none.fl_str_mv Artificial data
Data evolution
Data synthesis
Synthetic data generation
Synthetic test data
Synthetically generated data
Informática
Computer science
topic Artificial data
Data evolution
Data synthesis
Synthetic data generation
Synthetic test data
Synthetically generated data
Informática
Computer science
description Background High-level system testing of applications that use data from e-Government services as input requires test data that is real-life-like but where the privacy of personal information is guaranteed. Applications with such strong requirement include information exchange between countries, medicine, banking, and so on. This review aims to synthesise the current state-of-the-practice in this domain. Objectives The objective of this Systematic Review is to identify existing approaches for creating and evolving synthetic test data without using real-life raw data. Methods We followed well-known methodologies for conducting systematic literature reviews, including the ones from Kitchenham and PRISMA as well as guidelines for analysing the limitations of our review and its threats to validity. Results A variety of methods and tools exist for creating privacy-preserving test data. Our search found 1013 publications in IEEE Xplore, ACM Digital Library, and SCOPUS. We extracted data from 75 of those publications and identified 37 approaches that answer our research question partly. A common prerequisite for using these methods and tools is direct access to real-life data for data anonymization or synthetic test data generation. Nine existing synthetic test data generation approaches were identified that were closest to answering our research question. Nevertheless, further work would be needed to add the ability to evolve synthetic test data to the existing approaches. Conclusions None of the publications covered our requirements completely, only partially. Synthetic test data evolution is a field that has not received much attention from researchers but needs to be explored in Digital Government Solutions, especially since new legal regulations are being put in force in many countries.
publishDate 2025
dc.date.none.fl_str_mv 2025
2025-11-08
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
NA
http://purl.org/coar/version/c_be7fb7dd8ff6fe43
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10017/67590
https://dx.doi.org/10.1111/exsy.70164
url http://hdl.handle.net/10017/67590
https://dx.doi.org/10.1111/exsy.70164
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv Junta de Comunidades de Castilla-La Mancha http://dx.doi.org/10.13039/501100011698 Not available SBPLY%2F24%2F180225%2F000143
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv John Wiley & Sons
publisher.none.fl_str_mv John Wiley & Sons
dc.source.none.fl_str_mv reponame:e_Buah Biblioteca Digital Universidad de Alcalá
instname:Universidad de Alcalá (UAH)
instname_str Universidad de Alcalá (UAH)
reponame_str e_Buah Biblioteca Digital Universidad de Alcalá
collection e_Buah Biblioteca Digital Universidad de Alcalá
repository.name.fl_str_mv
repository.mail.fl_str_mv
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