Commonality-Driven Unit Test Generation

Björn Evers, Pouria Derakhshanfar, Xavier Devroey, Andy Zaidman

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


Various search-based test generation techniques have been proposed to automate the generation of unit tests fulfilling different criteria (e.g., line coverage, branch coverage, mutation score, etc.). Despite several advances made over the years, search-based unit test generation still suffers from a lack of guidance due to the limited amount of information available in the source code that, for instance, hampers the generation of complex objects. Previous studies introduced many strategies to address this issue, e.g., dynamic symbolic execution or seeding, but do not take the internal execution of the methods into account. In this paper, we introduce a novel secondary objective called commonality score, measuring how close the execution path of a test case is from reproducing a common or uncommon execution pattern observed during the operation of the software. To assess the commonality score, we implemented it in EvoSuite and evaluated its application on 150 classes from JabRef, an open-source software for managing bibliography references. Our results are mixed. Our approach leads to test cases that indeed follow common or uncommon execution patterns. However, if the commonality score can have a positive impact on the structural coverage and mutation score of the generated test suites, it can also be detrimental in some cases.

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
Number of pages16
ISBN (Print)9783030597610
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


Conference12th International Symposium on Search-Based Software Engineering, SSBSE 2020


  • Automated unit testing
  • Common paths coverage
  • Search-based software testing
  • Secondary objective


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