A Memetic Algorithm for test data generation of object-oriented software

Andrea Arcuri, Xin Yao

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

22 Citations (Scopus)


Generating test data for object-oriented (OO) software is a hard task. Little work has been done on the subject, and a lot of open problems still need to be investigated. In this paper we focus on container classes. They are used in almost every type of software, hence their reliability is of utmost importance. We present novel techniques to generate test data for container classes in an automatic way. A new representation with novel search operators is described and tested. A way to reduce the search space for OO software is presented. This is achieved by dynamically eliminating the functions that cannot give any further help from the search. Besides, the problem of applying the branch distances of disjunctions and conjunctions to OO software is solved. Finally, hill climbing, genetic algorithms and memetic algorithms are used and compared. Our empirical case study shows that our memetic algorithm outperforms the other algorithms.
Original languageEnglish
Title of host publication IEEE Congress on Evolutionary Computation, 2007. CEC 2007.
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages8
ISBN (Electronic)978-1-4244-1340-9
ISBN (Print)978-1-4244-1339-3
Publication statusPublished - 1 Sept 2007
EventIEEE Congress on Evolutionary Computation, 2007 (CEC 2007) - Singapore, Singapore
Duration: 25 Sept 200728 Sept 2007


ConferenceIEEE Congress on Evolutionary Computation, 2007 (CEC 2007)


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