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.
|Title of host publication||Search-Based Software Engineering - 12th International Symposium, SSBSE 2020|
|Editors||Aldeida Aleti, Annibale Panichella|
|Publication status||Accepted/In press - 2020|
Olsthoorn, M., Derakhshanfar, P., & Devroey, X. (Accepted/In press). An Application of Model Seeding to Search-based Unit Test Generation for Gson. In A. Aleti, & A. Panichella (Eds.), Search-Based Software Engineering - 12th International Symposium, SSBSE 2020 Springer.