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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Acknowledgement
This research was partially funded by the EU Horizon 2020 ICT-10-2016-RIA “STAMP” project (No. 731529).
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Olsthoorn, M., Derakhshanfar, P., Devroey, X. (2020). An Application of Model Seeding to Search-Based Unit Test Generation for Gson. In: Aleti, A., Panichella, A. (eds) Search-Based Software Engineering. SSBSE 2020. Lecture Notes in Computer Science(), vol 12420. Springer, Cham. https://doi.org/10.1007/978-3-030-59762-7_17
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DOI: https://doi.org/10.1007/978-3-030-59762-7_17
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