How Does Program Structure Impact the Effectiveness of the Crossover Operator in Evolutionary Testing?

by Phil McMinn

International Symposium on Search-Based Software Engineering (SSBSE 2010)


A more recent and expanded journal version of this paper is available — see "An Identification of Program Factors that Impact Crossover Performance in Evolutionary Test Input Generation for the Branch Coverage of C Programs".


Recent results in Search-Based Testing show that the relatively simple Alternating Variable hill climbing method outperforms Evolutionary Testing (ET) for many programs. For ET to perform well in covering an individual branch, a program must have a certain structure that gives rise to a fitness landscape that the crossover operator can exploit. This paper presents theoretical and empirical investigations into the types of program structure that result in such landscapes. The studies show that crossover lends itself to programs that process large data structures or have an internal state that is reached over a series of repeated function or method ... [more]


Reference

Phil McMinn. How Does Program Structure Impact the Effectiveness of the Crossover Operator in Evolutionary Testing?International Symposium on Search-Based Software Engineering (SSBSE 2010), pp. 9–18, 2010


Bibtex Entry
@inproceedings{McMinn2010,
  author    = "McMinn, Phil",
  title     = "How Does Program Structure Impact the Effectiveness of the Crossover Operator in Evolutionary Testing?",
  booktitle = "International Symposium on Search-Based Software Engineering (SSBSE 2010)",
  pages     = "9--18",
  year      = "2010",
  publisher = "IEEE"
}