Optimization of object oriented software testing using meta heuristic techniques

Abstract

Object Oriented paradigm is the most widely exploited design paradigm in the IT industry. The great functionalities of object orientation not only improve the developed software but also introduce a wide variety of problems in testing. For instance, not only the object but also the state of object impacts testing. Various other serious issues of testing come along with object orientation. Thus, this has always been a major concern of software testers. Hence, it is very important to optimize the process of object oriented software testing. An improved version of moth flame optimization algorithm based on the fermat s spiral has been proposed in this thesis, which is further applied on state transition diagrams (STD) of seven object oriented software applications to produce test paths. These test paths are compared with the original MFO algorithm and with other well-known nature-inspired meta-heuristic algorithms such as, firefly algorithm and ant colony optimization algorithm. A set of parameters is selected to evaluate and compare the results among these algorithms.