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

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.

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