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

Mitchell Olsthoorn, Pouria Derakhshanfar, Xavier Devroey

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

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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.
Original languageEnglish
Title of host publicationSearch-Based Software Engineering - 12th International Symposium, SSBSE 2020
EditorsAldeida Aleti, Annibale Panichella
Number of pages7
ISBN (Electronic)978-3-030-59762-7
ISBN (Print)9783030597610
Publication statusPublished - Oct 2020
Event12th Symposium on Search-Based Software Engineering - Online, Italy
Duration: 7 Oct 20208 Oct 2020
Conference number: 12

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12420 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference12th Symposium on Search-Based Software Engineering
Abbreviated titleSSBSE 2020
Internet address


  • Case study
  • Model seeding
  • Search-based software testing


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