Benefits of software measures for evolutionary white-box testing

ABSTRACT

White-box testing is an important method for the early detection of errors during software development. In this process test case generation plays a crucial role, defining appropriate and error-sensitive test data. The evolutionary white-box testing is a promising approach for the complete automation of structure-oriented test case generation. Here, test case generation can be completely automated with the help of evolutionary algorithms. However, problem cases exist in which the evolutionary test is not able to find valid test data. Thus, in the case of not achieving a test goal, it is not known whether this is due to non-executable program code or a problem case. This paper will investigate how successfully a software measure can support an evolutionary white-box test if the measure can predict the test effort. Hence, the termination criteria of evolutionary white-box testing can be adapted to test goals with problem cases in such a way that problematic test goals are either excluded from the test in advance or can be covered due to an adequate termination criteria according to a software measure. This could lead to an increase in efficiency and effectiveness of evolutionary white-box testing.

REFERENCES

• 1: Sthamer, H. The Automatic Generation of Software Test Data Using Genetic Algorithms. PhD Thesis, University of Glamorgan, Pontyprid, Wales, Great Britain, 1996.
• 2: André Baresel , Harmen Sthamer , Michael Schmidt, Fitness Function Design To Improve Evolutionary Structural Testing, Proceedings of the Genetic and Evolutionary Computation Conference, p.1329-1336, July 09-13, 2002
• 3: Wegener, J., Baresel, A., and Sthamer, H. Evolutionary Test Environment for Automatic Structural Testing. Information and Software Technology, vol. 43, 2001, 841--854.
• 4: Mark Harman , Lin Hu , Robert M. Hierons , André Baresel , Harmen Sthamer, Improving Evolutionary Testing By Flag Removal, Proceedings of the Genetic and Evolutionary Computation Conference, p.1359-1366, July 09-13, 2002
• 5: Harman, M., Hu, L., Hierons, R., Munro, M., Zhang, X., Dolado, J., Otero, M., and Wegener, J. A Post-Placement Side-Effect Removal Algorithm. Proceedings der IEEE International Conference, New York, 2002.

INDEX TERMS

Primary Classification:
  D. Software
  D.2 SOFTWARE ENGINEERING
      D.2.8 Metrics
          Subjects: Complexity measures

Additional Classification:
  D. Software
  D.2 SOFTWARE ENGINEERING
      D.2.8 Metrics
          Subjects: Performance measures

General Terms:
Measurement, Verification

Keywords:
automated test case generation, evolutionary testing, metric, software measurement