An Application of Model Seeding to Search-Based Unit Test Generation for Gson

Mitchell Olsthoorn; Pouria Derakhshanfar; Xavier Devroey

doi:10.1007/978-3-030-59762-7_17

An Application of Model Seeding to Search-Based Unit Test Generation for Gson

Mitchell Olsthoorn, Pouria Derakhshanfar, Xavier Devroey

Résultats de recherche: Contribution dans un livre/un catalogue/un rapport/dans les actes d'une conférence › Article dans les actes d'une conférence/un colloque

Résumé

Model seeding is a strategy for injecting additional information in a search-based test generation process in the form of models, representing usages of the classes of the software under test. These models are used during the search-process to generate logical sequences of calls whenever an instance of a specific class is required. Model seeding was originally proposed for search-based crash reproduction. We adapted it to unit test generation using EvoSuite and applied it to Gson, a Java library to convert Java objects from and to JSON. Although our study shows mixed results, it identifies potential future research directions.

langue originale	Anglais
titre	Search-Based Software Engineering - 12th International Symposium, SSBSE 2020, Proceedings
rédacteurs en chef	Aldeida Aleti, Annibale Panichella
Editeur	Springer Science and Business Media Deutschland GmbH
Pages	239-245
Nombre de pages	7
ISBN (imprimé)	9783030597610
Les DOIs	https://doi.org/10.1007/978-3-030-59762-7_17
Etat de la publication	Publié - 2020
Modification externe	Oui
Evénement	12th International Symposium on Search-Based Software Engineering, SSBSE 2020 - Bari, Italie Durée: 7 oct. 2020 → 8 oct. 2020

Série de publications

Nom	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12420 LNCS
ISSN (imprimé)	0302-9743
ISSN (Electronique)	1611-3349

Une conférence

Une conférence	12th International Symposium on Search-Based Software Engineering, SSBSE 2020
Pays/Territoire	Italie
La ville	Bari
période	7/10/20 → 8/10/20

Accès au document

10.1007/978-3-030-59762-7_17

Autres fichiers et liens

Link to publication in Scopus

1 Logiciel

YAMI: Yet Another Model Inference tool
Devroey, X., oct. 2014
Résultats de recherche: Forme non textuelle › Logiciel

Accès ouvert

Replication package of An Application of Model Seeding to Search-based Unit Test Generation for Gson
Derakhshanfar, P. (Créateur), Olsthoorn, M. (Créateur) & Devroey, X. (Créateur), Zenodo, 28 juil. 2020
DOI: 10.5281/zenodo.3963956
Ensemble de données

Contient cette citation

Olsthoorn, M., Derakhshanfar, P., & Devroey, X. (2020). An Application of Model Seeding to Search-Based Unit Test Generation for Gson. Dans A. Aleti, & A. Panichella (eds.), Search-Based Software Engineering - 12th International Symposium, SSBSE 2020, Proceedings (p. 239-245). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol 12420 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-59762-7_17

Olsthoorn, Mitchell ; Derakhshanfar, Pouria ; Devroey, Xavier. / An Application of Model Seeding to Search-Based Unit Test Generation for Gson. Search-Based Software Engineering - 12th International Symposium, SSBSE 2020, Proceedings. Editeur / Aldeida Aleti ; Annibale Panichella. Springer Science and Business Media Deutschland GmbH, 2020. p. 239-245 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Model seeding is a strategy for injecting additional information in a search-based test generation process in the form of models, representing usages of the classes of the software under test. These models are used during the search-process to generate logical sequences of calls whenever an instance of a specific class is required. Model seeding was originally proposed for search-based crash reproduction. We adapted it to unit test generation using EvoSuite and applied it to Gson, a Java library to convert Java objects from and to JSON. Although our study shows mixed results, it identifies potential future research directions.",

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Olsthoorn, M, Derakhshanfar, P & Devroey, X 2020, An Application of Model Seeding to Search-Based Unit Test Generation for Gson. Dans A Aleti & A Panichella (eds), Search-Based Software Engineering - 12th International Symposium, SSBSE 2020, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), VOL. 12420 LNCS, Springer Science and Business Media Deutschland GmbH, p. 239-245, 12th International Symposium on Search-Based Software Engineering, SSBSE 2020, Bari, Italie, 7/10/20. https://doi.org/10.1007/978-3-030-59762-7_17

An Application of Model Seeding to Search-Based Unit Test Generation for Gson. / Olsthoorn, Mitchell; Derakhshanfar, Pouria; Devroey, Xavier.

Search-Based Software Engineering - 12th International Symposium, SSBSE 2020, Proceedings. Ed. / Aldeida Aleti; Annibale Panichella. Springer Science and Business Media Deutschland GmbH, 2020. p. 239-245 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol 12420 LNCS).

Résultats de recherche: Contribution dans un livre/un catalogue/un rapport/dans les actes d'une conférence › Article dans les actes d'une conférence/un colloque

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AB - Model seeding is a strategy for injecting additional information in a search-based test generation process in the form of models, representing usages of the classes of the software under test. These models are used during the search-process to generate logical sequences of calls whenever an instance of a specific class is required. Model seeding was originally proposed for search-based crash reproduction. We adapted it to unit test generation using EvoSuite and applied it to Gson, a Java library to convert Java objects from and to JSON. Although our study shows mixed results, it identifies potential future research directions.

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Olsthoorn M, Derakhshanfar P, Devroey X. An Application of Model Seeding to Search-Based Unit Test Generation for Gson. Dans Aleti A, Panichella A, rédacteurs en chef, Search-Based Software Engineering - 12th International Symposium, SSBSE 2020, Proceedings. Springer Science and Business Media Deutschland GmbH. 2020. p. 239-245. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-030-59762-7_17

An Application of Model Seeding to Search-Based Unit Test Generation for Gson

Résumé

Série de publications

Une conférence

Accès au document

Autres fichiers et liens

Empreinte digitale

Résultat de recherche

YAMI: Yet Another Model Inference tool

Ensembles de données

Replication package of An Application of Model Seeding to Search-based Unit Test Generation for Gson

Contient cette citation