Automated Test Data Generation Using MEA-Graph Planning

  • Authors:

  • Manish Gupta;Farokh Bastani;Latifur Khan;I-Ling Yen

  • Affiliations:

  • University of Texas at Dallas;University of Texas at Dallas;University of Texas at Dallas;University of Texas at Dallas

  • Venue:
  • ICTAI '04 Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence
  • Year:
  • 2004

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Abstract

With the rapid growth in the development of modern and sophisticated software applications, such as Multimodal distributed systems, the complexity of software development processes has increased enormously, posing an urgent need for automation of some of these processes. One of the key software development process is system testing. In this paper, we evaluate the potential application of AI planning techniques in automating the testing process. We propose a framework for an automated planning system (APS) for applying AI planning techniques for automated testing of a software module. Using a comprehensive example, we demonstrate how the MEA-Graphplan (Means-Ends Analysis Graphplan) algorithm can be used to automatically generate test data (sequence of steps or actions) to transform the system from the current state to some desired goal state. MEA-Graph planning might prove to be computationally more efficient and effective than basic Graph Planning technique because here the planning graph is expanded in a goal-oriented manner using regression-matching graph constructed by regressing goals over actions that can overcome the problem of state-space explosion during graph expansion phase of the planning.