Ant Lion optimizer for state based object oriented testing

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 this whole process of object-oriented software testing. Numerous meta-heuristic algorithms have been exploited in the past to accomplish this goal. In this paper, we are presenting a novel nature-inspired algorithm, AntLion Optimization (ALO) to generate optimal test paths for object-oriented software. Here, we are accompanying model based testing instead of code based testing as, this helps in reducing the testing effort and cost by identifying the error and bugs in the design phase itself. The main goal of model based software testing is to cover all the transitions present in the system i.e. full path coverage must be there. ALO is a novel meta-heuristic algorithm based on the random movement of ants in search of food, constructing traps by antlions, tricking the ants in trap, catching preys and reconstruction of traps. We introduces a random factor in the fitness function to capture the random motion of ants in the search space in search of food. This algorithm has been applied on three object-oriented software applications and the result guarantees complete path coverage which is a primary goal of software testing.