An approach for software white box testing with genetic algorithm

O.A. Slabospickaya, O.G. Moroz

Abstract


A genetic algorithm is proposed to software testing efficiency improve through the most critical paths detecting in software program’s control flaw diagram. The results of algorithm’s probation are done. As exhaustive testing is often impossible even for medium-sized programs, usually only some separate paths of the program are checked that aren’t obligatory the most fault-prone. That’s why the more flexible approach for test data generating is developed allowing the most critical paths  to be checked first detection. An approach usage may can be instrumental in the rise of efficiency of testing


Keywords


testing; genetic algorithm

References


Андон Ф.И., Коваль Г.И., Коротун Т.М., Лаврищева Е.М., Суслов В.Ю. Основы инженерии качества программных систем. 2-e издание. - Киев.: Акадкмпериодика, 2007. – 672 с.

Липаев В.В. Тестирование программ. – М.: Радио и связь, 1986. – 296 с.

Beizer B. Software Testing Techniques. 2nd. Ed. Van Nostrand Reinhold, 1990. – 549 с.

Mathur A. P. Foundations of Software Testing: Fundamental Algorithms and Techniques, 1st edition Pearson Education 2008. С. 689.

ISO/IEC 12207: 1995. Information technologies. Software life cycle processes. - 61p.

Pfleeger S. L.: Software Engineering: Theory and Practice. 2nd Edition, Prentice-Hall, 2001. - 659p.

Mansour N., Salame M. Data Generation for Path Testing , Software Quality Journal, 12, 121–136, 2004.

Wegener J., Baresel A., Sthamer H. Suitability of Evolutionary Algorithms for Evolutionary Testing // In Proceedings of the 26th Annual International Computer Software and Applications Conference, Oxford, England, August 26-29, 2002.

Berndt D.J., Watkins A. Investigating the Performance of Genetic Algorithm-Based Software Test Case Generation // In Proceedings of the Eighth IEEE International Symposium on High Assurance Systems Engineering (HASE'04), pp. 261-262, University of South Florida, March 25-26, 2004.

Korel B. Automated software test data generation. IEEE Transactions on Software Engineering, 16(8), August 1990.

Jones B.F., Sthamer H.H., Eyres D.E. Automatic structural testing using genetic algorithms. Software Engineering Journal, pages 299-306, September, 1996.

Harman M., Jones B. Search-based Software engineering // Information and software technology 43(14), -2001, - 833-839 c.

Last M., Eyal S., Kandel A. Effective Black-Box Testing with Genetic Algorithms // Lecture notes in computer science. -2006, pages 134-148.

Alander J., Mantere T., Turunen P. Genetic Algorithm Based Software Testing. - http://cite-seer.ist.psu.edu/40769.html.

Berndt D.J., Fisher J., Johnson L., Pinglikar J., Watkins A. Breeding Software Test Cases with Genetic Algorithms // In Proceedings of the Thirty-Sixth Hawaii International Conference on System Sciences (HICSS-36), Hawaii, January 2003.

Allen F. E. Control flow analysis / ACM SIGPLAN Notices - Proceedings of a symposium on Compiler optimization. Volume 5 Issue 7, July 1970 c.1-19.

Гладков Л.А., Курейчик В.В., Курейчик В.М. Генетические алгоритмы. – М.: ФИЗМАТЛИТ, 2006. – 320 с.


Refbacks

  • There are currently no refbacks.