AI Planner Assisted Test Generation

  • Authors:

  • Anneliese K. Amschler Andrews;Chunhui Zhu;Michael Scheetz;Eric Dahlman;Adele E. Howe

  • Affiliations:

  • School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA 99163 aandrews@eecs.wsu.edu;Computer Science Department, Colorado State University, Fort Collins, CO 80523;Computer Science Department, Colorado State University, Fort Collins, CO 80523;Computer Science Department, Colorado State University, Fort Collins, CO 80523 dahlman@cs.colostate.edu;Computer Science Department, Colorado State University, Fort Collins, CO 80523 howe@cs.colostate.edu

  • Venue:
  • Software Quality Control
  • Year:
  • 2002

Quantified Score

Hi-index 0.00

Visualization

Abstract

This paper describes an AI planner assisted approach to generate test cases for system testing based on high level test objectives. We use four levels of test generation: the metaprocessor, the preprocessor, the AI planner, and the postprocessor levels. Test generation is based on an extended UML model of the system under test and a mapping of high-level test objectives into initial and goal conditions of the planner. Test objectives are derived from a series of interviews with professional testers. We suggest various options for test criteria related to test objectives. The AI planner was used to generate hundreds of test cases for a robot controlled tape silo. The planner generated tests within a reasonable time. It was successful for each test objective given.