A method dependence relations guided genetic algorithm

Ali Aburas, Alex Groce

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations


Search based test generation approaches have already been shown to be effective for generating test data that achieves high code coverage for object-oriented programs. In this paper, we present a new search-based approach, called GAMDR, that uses a genetic algorithm (GA) to generate test data. GAMDR exploits method dependence relations (MDR) to narrow down the search space and direct mutation operators to the most beneficial regions for achieving high branch coverage.We compared GAMDR’s effectiveness with random testing, EvoSuite, and a simple GA. The tests generated by GAMDR achieved higher branch coverage.

Original languageEnglish (US)
Title of host publicationSearch Based Software Engineering - 8th International Symposium, SSBSE 2016, Proceedings
EditorsFederica Sarro, Kalyanmoy Deb
Number of pages7
ISBN (Print)9783319471051
StatePublished - 2016
Externally publishedYes
Event8th International Symposium on Search Based Software Engineering, SSBSE 2016 - Raleigh, United States
Duration: Oct 8 2016Oct 10 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9962 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference8th International Symposium on Search Based Software Engineering, SSBSE 2016
Country/TerritoryUnited States


  • Genetic algorithm
  • Java testing
  • SBST
  • Search space reduction

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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