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Nature-Inspired Approaches in Software Testing Optimization

Software development is considered to be a fast growing industry that drives numerous modern world domains forward. At the same time, software testing and quality assurance, that is equally important branch of software industry, resides in the shadows, far away from the spotlights. The goal of the software testing is to detect defects in the software and ensure that it has sufficient quality prior to the release. The main objective of the software testing can be defined as finding the minimal test suite that is still efficient enough to maintain the certain software quality. Since the process of test cases generation performs a search for an optimal test suite in a huge search space, and keeping in mind that the swarm intelligence metaheuristics have already proven to be efficient optimizers in other domains, it makes sense to apply swarm intelligence algorithms to the process of test cases generation as well. By utilizing this approach, it could be also possible to reduce the cost required for testing. This paper provides a survey of recent applications of swarm intelligence algorithms in the domain of software testing.