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

Mitchell Olsthoorn, Pouria Derakhshanfar, Xavier Devroey

Research output: Contribution in Book/Catalog/Report/Conference proceedingConference contribution

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.

Original languageEnglish
Title of host publicationSearch-Based Software Engineering - 12th International Symposium, SSBSE 2020, Proceedings
EditorsAldeida Aleti, Annibale Panichella
PublisherSpringer Science and Business Media Deutschland GmbH
Pages239-245
Number of pages7
ISBN (Print)9783030597610
DOIs
Publication statusPublished - 2020
Externally publishedYes
Event12th International Symposium on Search-Based Software Engineering, SSBSE 2020 - Bari, Italy
Duration: 7 Oct 20208 Oct 2020

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

Conference

Conference12th International Symposium on Search-Based Software Engineering, SSBSE 2020
CountryItaly
CityBari
Period7/10/208/10/20

Keywords

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

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