On the Use of Uniform Random Generation of Automata for Testing

  • Authors:
  • Frédéric Dadeau;Jocelyn Levrey;Pierre-Cyrille Héam

  • Affiliations:
  • LIFC - INRIA CASSIS Project, 16 route de Gray, 25030 Besançon, France;LIFC - INRIA CASSIS Project, 16 route de Gray, 25030 Besançon, France;LSV - ENS de Cachan,CNRS UMR 8643, 61, avenue du Président Wilson, 94235 Cachan cedex, France and LIFC - INRIA CASSIS Project, 16 route de Gray, 25030 Besançon, France

  • Venue:
  • Electronic Notes in Theoretical Computer Science (ENTCS)
  • Year:
  • 2009

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Abstract

Developing efficient and automatic testing techniques is one of the major challenges facing software validation community. In this paper, we show how a uniform random generation process of finite automata, developed in a recent work by Bassino and Nicaud, is relevant for many faces of automatic testing. The main contribution is to show how to combine two major testing approaches: model-based testing and random testing. This leads to a new testing technique successfully experimented on a realistic case study. We also illustrate how the power of random testing, applied on a Chinese Postman Problem implementation, points out an error in a well-known algorithm. Finally, we provide some statistics on model-based testing algorithms.