Optimized Test Sequence Generation from Usage Models using Ant Colony Optimization

Abstract

Software Testing is the process of testing the software in order to ensure that it is free of errors and produces the desired outputs in any given situation. Model based testing is an approach in which software is viewed as a set of states. A usage model describes software on the basis of its statistical usage data. One of the major problems faced in such an approach is the generation of optimal sets of test sequences. The model discussed in this paper is a Markov chain based usage model. The analytical operations and results associated with Markov chains make them an appropriate choice for checking the feasibility of test sequences while they are being generated. The statistical data about the estimated usage has been used to build a stochastic model of the software under test. This paper proposes a technique to generate optimized test sequences from a markov chain based usage model. The proposed technique uses ant colony optimization as its basis and also incorporates factors like cost and criticality of various states in the model. It further takes into consideration the average number of visits to any state and the trade-off between cost considerations and optimality of the test coverage.