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 language | English |
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Title of host publication | Search-Based Software Engineering - 12th International Symposium, SSBSE 2020 |
Editors | Aldeida Aleti, Annibale Panichella |
Publisher | Springer |
Pages | 239-245 |
Number of pages | 7 |
ISBN (Electronic) | 978-3-030-59762-7 |
ISBN (Print) | 9783030597610 |
DOIs | |
Publication status | Published - Oct 2020 |
Event | 12th Symposium on Search-Based Software Engineering - Online, Italy Duration: 7 Oct 2020 → 8 Oct 2020 Conference number: 12 http://ssbse2020.di.uniba.it |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 12420 LNCS |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | 12th Symposium on Search-Based Software Engineering |
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Abbreviated title | SSBSE 2020 |
Country/Territory | Italy |
Period | 7/10/20 → 8/10/20 |
Internet address |
Keywords
- Case study
- Model seeding
- Search-based software testing