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

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

Olsthoorn, Mitchell (TU Delft Software Engineering)
Derakhshanfar, P. (TU Delft Software Engineering)
Devroey, Xavier (TU Delft Software Engineering)

Aleti, Aldeida (editor)
Panichella, Annibale (editor)

2020-10

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.

Case study
Model seeding
Search-based software testing

http://resolver.tudelft.nl/uuid:c04602b6-4987-4007-85ca-53698c750c3e

https://doi.org/10.1007/978-3-030-59762-7_17

Springer

9783030597610

Search-Based Software Engineering - 12th International Symposium, SSBSE 2020

12th Symposium on Search-Based Software Engineering, 2020-10-07 → 2020-10-08, Online, , Italy

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 0302-9743, 12420 LNCS

Institutional Repository

conference paper

© 2020 Mitchell Olsthoorn, P. Derakhshanfar, Xavier Devroey