Nature-inspired techniques for conformance testing of object-oriented software

Soft computing offers a plethora of techniques for dealing with hard optimization problems. In particular, nature based techniques have been shown to be very efficient in optimization applications. The present paper investigates the suitability of various nature-inspired meta-heuristics (genetic algorithms, evolutionary programming and ant-colony systems) to the problem of software testing. The present study is part of the nature-inspired techniques for object-oriented testing (NITOT) environment. It aims at addressing the problem of conformance testing of object-oriented software to its specification expressed in terms of finite state machines. Detailed description, adaptation and evaluation of the various nature-inspired meta-heuristics are discussed showing their potential in this context of conformance testing.