Title
Random or evolutionary search for object-oriented test suite generation?
Abstract
An important aim in software testing is constructing a test suite with high structural code coverage, that is, ensuring that most if not all of the code under test have been executed by the test cases comprising the test suite. Several search-based techniques have proved successful at automatically generating tests that achieve high coverage. However, despite the well-established arguments behind using evolutionary search algorithms (eg, genetic algorithms) in preference to random search, it remains an open question whether the benefits can actually be observed in practice when generating unit test suites for object-oriented classes. In this paper, we report an empirical study on the effects of using evolutionary algorithms (including a genetic algorithm and chemical reaction optimization) to generate test suites, compared with generating test suites incrementally with random search. We apply the EVOSUITE unit test suite generator to 1000 classes randomly selected from the SF110 corpus of open-source projects. Surprisingly, the results show that the difference is much smaller than one might expect: While evolutionary search covers more branches of the type where standard fitness functions provide guidance, we observed that, in practice, the vast majority of branches do not provide any guidance to the search. These results suggest that, although evolutionary algorithms are more effective at covering complex branches, a random search may suffice to achieve high coverage of most object-oriented classes.
Year
DOI
Venue
2018
10.1002/stvr.1660
SOFTWARE TESTING VERIFICATION & RELIABILITY
Keywords
Field
DocType
automated software testing,automated test generation,chemical reaction optimization,genetic algorithms,random search,search-based software testing
Code coverage,Test suite,Random search,Search algorithm,Evolutionary algorithm,Suite,Computer science,Theoretical computer science,Test case,Genetic algorithm
Journal
Volume
Issue
ISSN
28.0
4
0960-0833
Citations 
PageRank 
References 
1
0.35
27
Authors
7
Name
Order
Citations
PageRank
Sina Shamshiri1813.59
José Miguel Rojas210.35
Luca Gazzola3453.10
Gordon Fraser42625116.22
phil mcminn5238497.58
Leonardo Mariani623324.60
Andrea Arcuri7263092.48