Incremental test data generation for database queries

Testing database applications is a complex task since it involves designing test databases with meaningful test data in order to reveal faults and, at the same time, with a small size in order to carry out the testing process in an efficient way. This paper presents an automated approach to generati...

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Autores: Suárez Cabal, María José|||0000-0001-8262-2871, Riva Álvarez, Claudio A. de la|||0000-0001-5592-9683, Tuya González, Pablo Javier|||0000-0002-1091-934X, Blanco Aguirre, Raquel|||0000-0003-2855-0760
Tipo de recurso: artículo
Fecha de publicación:2017
País:España
Institución:Universidad de Oviedo (UNIOVI)
Repositorio:RUO. Repositorio Institucional de la Universidad de Oviedo
Idioma:inglés
OAI Identifier:oai:digibuo.uniovi.es:10651/57781
Acceso en línea:http://hdl.handle.net/10651/57781
https://dx.doi.org/10.1007%2Fs10515-017-0212-7
Access Level:acceso abierto
Palabra clave:software testing
database testing
test database generation
test coverage
constraint satisfaction problem (CSP)
software engineering
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spelling Incremental test data generation for database queriesSuárez Cabal, María José|||0000-0001-8262-2871Riva Álvarez, Claudio A. de la|||0000-0001-5592-9683Tuya González, Pablo Javier|||0000-0002-1091-934XBlanco Aguirre, Raquel|||0000-0003-2855-0760software testingdatabase testingtest database generationtest coverageconstraint satisfaction problem (CSP)software engineeringTesting database applications is a complex task since it involves designing test databases with meaningful test data in order to reveal faults and, at the same time, with a small size in order to carry out the testing process in an efficient way. This paper presents an automated approach to generating test data (test relational databases and test inputs for query parameters) for a set of SQL queries, with the aim of covering test requirements as obtained from said queries. The test data generation follows an incremental approach where, in each increment, test data are generated to cover a test requirement by re-using test data previously generated for other test requirements. The test data generation for each test requirement is formulated as a constraint satisfaction problem, where constraints are derived from the test requirement, initial database states and previously generated test data. The generation process is fully automated and supports the execution on complex queries and databases. Evaluation is carried out on a real life application, and the results show that small-size generated test relational databases achieve high coverage scores for the queries under test in a short generating time.Projects TIN2013-46928-C3-1-R and TIN2016- 76956-C3-1-R, funded by the Spanish Ministry of Science and Technology, and GRUPIN14-007, funded by the Principality of Asturias (Spain) and ERDF funds.Springer Nature20172017-12-01journal articlehttp://purl.org/coar/resource_type/c_6501AMhttp://purl.org/coar/version/c_ab4af688f83e57aainfo:eu-repo/semantics/articlehttp://hdl.handle.net/10651/57781https://dx.doi.org/10.1007%2Fs10515-017-0212-7reponame:RUO. Repositorio Institucional de la Universidad de Oviedoinstname:Universidad de Oviedo (UNIOVI)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessoai:digibuo.uniovi.es:10651/577812026-06-07T06:38:51Z
dc.title.none.fl_str_mv Incremental test data generation for database queries
title Incremental test data generation for database queries
spellingShingle Incremental test data generation for database queries
Suárez Cabal, María José|||0000-0001-8262-2871
software testing
database testing
test database generation
test coverage
constraint satisfaction problem (CSP)
software engineering
title_short Incremental test data generation for database queries
title_full Incremental test data generation for database queries
title_fullStr Incremental test data generation for database queries
title_full_unstemmed Incremental test data generation for database queries
title_sort Incremental test data generation for database queries
dc.creator.none.fl_str_mv Suárez Cabal, María José|||0000-0001-8262-2871
Riva Álvarez, Claudio A. de la|||0000-0001-5592-9683
Tuya González, Pablo Javier|||0000-0002-1091-934X
Blanco Aguirre, Raquel|||0000-0003-2855-0760
author Suárez Cabal, María José|||0000-0001-8262-2871
author_facet Suárez Cabal, María José|||0000-0001-8262-2871
Riva Álvarez, Claudio A. de la|||0000-0001-5592-9683
Tuya González, Pablo Javier|||0000-0002-1091-934X
Blanco Aguirre, Raquel|||0000-0003-2855-0760
author_role author
author2 Riva Álvarez, Claudio A. de la|||0000-0001-5592-9683
Tuya González, Pablo Javier|||0000-0002-1091-934X
Blanco Aguirre, Raquel|||0000-0003-2855-0760
author2_role author
author
author
dc.subject.none.fl_str_mv software testing
database testing
test database generation
test coverage
constraint satisfaction problem (CSP)
software engineering
topic software testing
database testing
test database generation
test coverage
constraint satisfaction problem (CSP)
software engineering
description Testing database applications is a complex task since it involves designing test databases with meaningful test data in order to reveal faults and, at the same time, with a small size in order to carry out the testing process in an efficient way. This paper presents an automated approach to generating test data (test relational databases and test inputs for query parameters) for a set of SQL queries, with the aim of covering test requirements as obtained from said queries. The test data generation follows an incremental approach where, in each increment, test data are generated to cover a test requirement by re-using test data previously generated for other test requirements. The test data generation for each test requirement is formulated as a constraint satisfaction problem, where constraints are derived from the test requirement, initial database states and previously generated test data. The generation process is fully automated and supports the execution on complex queries and databases. Evaluation is carried out on a real life application, and the results show that small-size generated test relational databases achieve high coverage scores for the queries under test in a short generating time.
publishDate 2017
dc.date.none.fl_str_mv 2017
2017-12-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
AM
http://purl.org/coar/version/c_ab4af688f83e57aa
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10651/57781
https://dx.doi.org/10.1007%2Fs10515-017-0212-7
url http://hdl.handle.net/10651/57781
https://dx.doi.org/10.1007%2Fs10515-017-0212-7
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
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
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Springer Nature
publisher.none.fl_str_mv Springer Nature
dc.source.none.fl_str_mv reponame:RUO. Repositorio Institucional de la Universidad de Oviedo
instname:Universidad de Oviedo (UNIOVI)
instname_str Universidad de Oviedo (UNIOVI)
reponame_str RUO. Repositorio Institucional de la Universidad de Oviedo
collection RUO. Repositorio Institucional de la Universidad de Oviedo
repository.name.fl_str_mv
repository.mail.fl_str_mv
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