Paper Info
Title
An empirical evaluation of evolutionary algorithms for unit test suite generation.
Abstract
Evolutionary algorithms have been shown to be effective at generating unit test suites optimised for code coverage. While many aspects of these algorithms have been evaluated in detail (e.g., test length and different kinds of techniques aimed at improving performance, like seeding), the influence of the specific algorithms has to date seen less attention in the literature. As it is theoretically impossible to design an algorithm that is best on all possible problems, a common approach in software engineering problems is to first try a Genetic Algorithm, and only afterwards try to refine it or compare it with other algorithms to see if any of them is more suited for the addressed problem. This is particularly important in test generation, since recent work suggests that random search may in practice be equally effective, whereas the reformulation as a many-objective problem seems to be more effective. To shed light on the influence of the search algorithms, we empirically evaluate six different algorithms on a selection of non-trivial open source classes. Our study shows that the use of a test archive makes evolutionary algorithms clearly better than random testing, and it confirms that the many-objective search is the most effective.
Year
DOI
Venue
2018
10.1016/j.infsof.2018.08.010
Information and Software Technology
Keywords
Field
DocType
Evolutionary algorithms,Test suite generation,Empirical study
Code coverage,Random search,Data mining,Search algorithm,Random testing,Evolutionary algorithm,Suite,Computer science,Unit testing,Artificial intelligence,Genetic algorithm,Machine learning
Journal
Volume
ISSN
Citations 
104
0950-5849
15
PageRank 
References 
Authors
0.60
33
6
Name
Order
Citations
PageRank
José Creissac Campos147342.36
Yan Ge2342.60
Nasser Albunian3150.60
Gordon Fraser42625116.22
Marcelo Medeiros Eler59014.50
Andrea Arcuri6263092.48