Improving evolutionary real-time testing

  • Authors:

  • Marouane Tlili;Stefan Wappler;Harmen Sthamer

  • Affiliations:

  • Daimler Chrysler AG, Berlin, Germany;DaimlerChrysler Automotive IT Institute, Berlin, Germany;Daimler Chrysler AG, Berlin, Germany

  • Venue:
  • Proceedings of the 8th annual conference on Genetic and evolutionary computation
  • Year:
  • 2006

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Abstract

Embedded systems are often used in a safety-critical context, e.g. in airborne or vehicle systems. Typically, timing constraints must be satisfied so that real-time embedded systems work properly and safely. Execution time testing involves finding the best and worst case execution times to determine if timing constraints are respected. Evolutionary real-time testing (ERTT) is used to dynamically search for the extreme execution times. It can be shown that ERTT outperforms the traditional methods based on static analysis. However, during the evolutionary search, some parts of the source code are never accessed. Moreover, it turns out that ERTT delivers different extreme execution times in a high number of generations for the same test object, the results are neither reliable nor efficient. We propose a new approach to ERTT which makes use of seeding the evolutionary algorithm with test data achieving a high structural coverage. Using such test data ensures a comprehensive exploration of the search space and leads to rise the confidence in the results. We present also another improvement method based on restricting the range of the input variables in the initial population in order to reduce the search space. Experiments with these approaches demonstrate an increase of reliability in terms of constant extreme execution times and a gain in efficiency in terms of number of generations needed.