There is a large need for effective and efficient testing processes and tools for mobile applications, due to their continuous evolution and to the sensitivity of their users to failures. Industries and researchers focus their effort to the realization of effective fully automatic testing techniques for mobile applications. Many of the proposed testing techniques lack in efficiency because their algorithms cannot be executed in parallel. In particular, Active Learning testing techniques usually relay on sequential algorithms. In this paper we propose a Active Learning technique for the fully automatic exploration and testing of Android applications, that parallelizes and improves a general algorithm proposed in the literature. The novel parallel algorithm has been implemented in the context of a prototype tool exploiting a component-based architecture, and has been experimentally evaluated on 3 open source Android applications by varying different deployment configurations. The measured results have shown the feasibility of the proposed technique and an average saving in testing time between 33% (deploying two testing resources) and about 80% (deploying 12 testing resources).
展开▼
