Abstract
The use of artificial intelligent (AI) techniques for testing software applications has been investigated for over a decade. This paper proposes a framework to assist test managers to evaluate the use of AI techniques as a potential tool to test software. The framework is designed to facilitate decision making and provoke the decision maker into a better understanding of the use of AI techniques as a testing tool. We provide an overview of the framework and its components. Fuzzy Cognitive Maps (FCMs) are employed to evaluate the framework and make decision analysis easier, and therefore help the decision making process about the use of AI techniques to test software. What-if analysis is used to explore and illustrate the general application of the framework.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Dick, S., Kandel, A.: Computational intelligence in software quality assurance. Series in machine perception and artificial intelligence, vol. 63. World Scientific, Hackensack (2005)
Hailpern, B., Santhanam, P.: Software debugging, testing, and verification. IBM Systems Journal 41(1), 4–12 (2002)
Institute of Electrical and Electronics Engineers. IEEE standard for software test documentation. USA (IEEE Std. 829-1983) (1983)
Institute of Electrical and Electronics Engineers. IEEE standard glossary of software engineering terminology. USA (IEEE Std. 610.12-1990) (1990)
Patton, R.: Software testing, 2nd edn. Sams Publishing, Indiana (2006)
Dustin, E., Rashka, J., Paul, J.: Automated software testing: Introduction, management, and performance. Addison-Wesley, Reading (1999)
Harman, M., McMinn, P.: A theoretical & empirical analysis of evolutionary testing and hill climbing for structural test data generation. In: Proceedings of the 2007 International Symposium on Software Testing and Analysis, pp. 73–83 (2007)
Hermadi, I., Ahmed, M.A.: Genetic algorithm based test data generator. In: The 2003 Congress on Evolutionary Computation, vol. 1, pp. 85–91 (2003)
Howe, A.E., Von Mayrhauser, A., Mraz, R.T.: Test case generation as an AI planning problem. Automated Software Engineering 4(1), 77–106 (1997)
Michael, C.C., McGraw, G., Schatz, M.A.: Generating software test data by evolution. IEEE Transactions on Software Engineering 27(12), 1085–1110 (2001)
Kim, J.-M., Porter, A., Rothermel, G.: An empirical study of regression test application frequency. Software Testing, Verification and Reliability 15(4), 257–279 (2005)
Kosko, B.: Fuzzy engineering. Prentice Hall, Upper Saddle River (1997)
Ammann, P., Offutt, J.: Introduction to software testing. Cambridge University Press, USA (2008)
Tian, J.: Software quality engineering: Testing, quality assurance, and quantifiable improvement. Wiley, Hoboken (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer Berlin Heidelberg
About this paper
Cite this paper
Larkman, D., Mohammadian, M., Balachandran, B., Jentzsch, R. (2010). General Application of a Decision Support Framework for Software Testing Using Artificial Intelligence Techniques. In: Phillips-Wren, G., Jain, L.C., Nakamatsu, K., Howlett, R.J. (eds) Advances in Intelligent Decision Technologies. Smart Innovation, Systems and Technologies, vol 4. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14616-9_5
Download citation
DOI: https://doi.org/10.1007/978-3-642-14616-9_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-14615-2
Online ISBN: 978-3-642-14616-9
eBook Packages: EngineeringEngineering (R0)