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

Mitchell Olsthoorn, Pouria Derakhshanfar, Xavier Devroey

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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 originaleAnglais
titreSearch-Based Software Engineering - 12th International Symposium, SSBSE 2020, Proceedings
rédacteurs en chefAldeida Aleti, Annibale Panichella
EditeurSpringer Science and Business Media Deutschland GmbH
Pages239-245
Nombre de pages7
ISBN (imprimé)9783030597610
Les DOIs
Etat de la publicationPublié - 2020
Modification externeOui
Evénement12th International Symposium on Search-Based Software Engineering, SSBSE 2020 - Bari, Italie
Durée: 7 oct. 20208 oct. 2020

Série de publications

NomLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12420 LNCS
ISSN (imprimé)0302-9743
ISSN (Electronique)1611-3349

Une conférence

Une conférence12th International Symposium on Search-Based Software Engineering, SSBSE 2020
PaysItalie
La villeBari
période7/10/208/10/20

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