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Software testing processes as a linear dynamic system

Authors:

Ke LiuAuthors Info & Claims

Information Sciences—Informatics and Computer Science, Intelligent Systems, Applications: An International Journal, Volume 178, Issue 6

Pages 1558 - 1597

https://doi.org/10.1016/j.ins.2007.10.024

Published: 20 March 2008 Publication History

Abstract

Software testing is essential for software reliability improvement and assurance, and the processes of software testing are intrinsically dynamic. However they are seldom investigated in a mathematically rigorous manner. In this paper a theoretical study is presented to examine the dynamic behavior of software testing. More specifically, a set of simplifying assumptions is adopted to formulate and quantify the software testing processes. The mathematical formulae for the expected number of observed software failures are rigorously derived, the bounds and trends of the expected number of observed software failures are analyzed, and the variance of the number of observed software failures is examined. On the other hand, it is demonstrated that under the simplifying assumptions, the software testing processes can be treated as a linear dynamic system. This suggests that the software testing processes could be classified as linear or non-linear, and there be intrinsic link between software testing and system dynamics.

References

[1]

Binder, R.V., Testing Object-Oriented Systems: Models, Patterns, and Tools. 2000. Addison-Wesley.

Digital Library

[2]

Briand, L.C., Wust, J. and Lounis, H., Replicated case studies for investigating quality factors in object-oriented design. Empirical Software Engineering. v6. 11-58.

Digital Library

[3]

Cai, K.Y., Software Defect and Operational Profile Modeling. 1998. Kluwer Academic Publishers.

Digital Library

[4]

Cai, K.Y., Software reliability experimentation and control. Journal of Computer Science and Technology. v21 i5. 697-707.

[5]

Cai, K.Y., Dong, Z., Liu, K. and Bai, C.G., A mathematical modeling framework for software reliability testing. International Journal of General Systems. v36 i4. 399-463.

[6]

Cai, K.Y., Gu, B., Hu, H. and Li, Y.C., Adaptive software testing with fixed-memory feedback. Journal of Systems and Software. v80. 1328-1348.

Digital Library

[7]

Cangussu, J.W., DeCarlo, R.A. and Mathur, A.P., A formal model for the software test process. IEEE Transactions on Software Engineering. v27 i8. 782-796.

Digital Library

[8]

Cangussu, J.W., DeCarlo, R.A. and Mathur, A.P., Using sensitivity analysis to validate a state variable model of the software test process. IEEE Transactions on Software Engineering. v29 i5. 430-443.

Digital Library

[9]

A. Cater-Steel, M. Toleman, T. Rout, Addressing the changes of replication of surveys in software engineering research, in: Proceedings of the International Symposium on Empirical Software Engineering, 2005, pp. 204-213.

[10]

Chen, C.T., Linear System Theory and Design. 1984. CBS College Publishing.

Digital Library

[11]

Dick, S., Bethel, C.L. and Kandel, A., Software-reliability modeling: the case for deterministic behavior. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Human. v37 i1. 106-119.

Digital Library

[12]

R. Findeisen, F. Allgower, An introduction to nonlinear model predictive control, http://www.wfw.wtb.tue.nl/benelux2002/nmpc.pdf.

[13]

Gutjahr, W.J., Software dependability evaluation based on Markov usage models. Performance Evaluation. v40. 199-222.

Digital Library

[14]

K. Henningsson, C. Wohlin, Assuring fault classification agreement - an empirical evaluation, in: Proceedings of 2004 International Symposium on Empirical Software Engineering, 2004, pp. 95-104.

Digital Library

[15]

Lin Huang, Linear Algebra in the Theory of Systems and Control, Science Press, 1984 (Theorem 3.3.5, p. 110).

[16]

Janes, A., Scotto, M., Pedrycz, W., Russo, B., Stefanovic, M. and Succi, G., Identification of defect-prone classes in telecommunication software systems using design metrics. Information Sciences. v176. 3711-3734.

Digital Library

[17]

Juristo, N., Moreno, A.M. and Vegas, S., Reviewing 25 years of testing technique experiments. Empirical Software Engineering. v9. 7-44.

Digital Library

[18]

Khalil, H.K., Nonlinear Systems. 2001. third ed. Prentice-Hall.

[19]

Lee, C.H., Kim, Y.T. and Park, D.H., S-shaped software reliability growth models derived from stochastic differential equations. IIE Transactions. v36. 1193-1199.

[20]

In: Lyu, M.R. (Ed.), Handbook of Software Reliability Engineering, McGraw-Hill.

Digital Library

[21]

Minc, H., Nonnegative Matrices. 1988. Wiley, New York.

[22]

Poore, J.H., Walton, G.H. and Whittaker, J.A., A constraint-based approach to the representation of software usage models. Information and Software Technology. v42. 825-833.

[23]

Rothermel, G., Elbaum, S., Malishevsky, A.G., Kallakui, P. and Qiu, X., On test suite composition and cost-effective regression testing. ACM Transactions on Software Engineering and Methodology. v13 i3. 277-331.

Digital Library

[24]

Sjoberg, D.I.K., Hannay, J.E., Hansen, O., Kampenes, V.B., Karahasanovic, A., Liborg, N.K. and Rekdal, A.C., A survey of controlled experiments in software engineering. IEEE Transactions on Software Engineering. v31 i9. 733-753.

Digital Library

[25]

Whittaker, J.A., What is software testing? And why is it so hard?. IEEE Software. iJanuary/February. 70-79.

Digital Library

[26]

Whittaker, J.A. and Poore, J.H., Markov analysis of software specifications. ACM Transactions on Software Engineering and Methodology. v2. 93-106.

Digital Library

[27]

Whittaker, J.A. and Thomason, M.G., A Markov chain model for statistical software testing. IEEE Transactions on Software Engineering. v20 i10. 812-824.

Digital Library

[28]

Xie, M., Software Reliability Modeling. 1991. World Scientific.

[29]

Yamada, S., Kimura, M., Tanaka, H. and Osaki, S., Software reliability measurement and assessment with stochastic differential equations. IEICE Transactions, Fundamentals. vE77-A. 109-115.

Cited By

Ivanov VReznik ASucci G(2018)Comparing the reliability of software systemsInformation Sciences: an International Journal10.1016/j.ins.2017.08.079423:C(398-411)Online publication date: 1-Jan-2018
https://dl.acm.org/doi/10.1016/j.ins.2017.08.079
Moadab SRashidi H(2016)Automatic path-oriented test data generation by boundary hypercuboidsJournal of King Saud University - Computer and Information Sciences10.1016/j.jksuci.2015.05.00228:1(82-97)Online publication date: 1-Jan-2016
https://dl.acm.org/doi/10.1016/j.jksuci.2015.05.002
Cao PDong ZLiu KCai K(2013)Quantitative effects of software testing on reliability improvement in the presence of imperfect debuggingInformation Sciences: an International Journal10.1016/j.ins.2012.06.034218(119-132)Online publication date: 1-Jan-2013
https://dl.acm.org/doi/10.1016/j.ins.2012.06.034
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Software testing processes as a linear dynamic system

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Published In

cover image Information Sciences: an International Journal

Information Sciences: an International Journal Volume 178, Issue 6

March, 2008

361 pages

ISSN:0020-0255

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Copyright © Elsevier Inc. © 2007.

Publisher

Elsevier Science Inc.

United States

Publication History

Published: 20 March 2008

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Cited By

Ivanov VReznik ASucci G(2018)Comparing the reliability of software systemsInformation Sciences: an International Journal10.1016/j.ins.2017.08.079423:C(398-411)Online publication date: 1-Jan-2018
https://dl.acm.org/doi/10.1016/j.ins.2017.08.079
Moadab SRashidi H(2016)Automatic path-oriented test data generation by boundary hypercuboidsJournal of King Saud University - Computer and Information Sciences10.1016/j.jksuci.2015.05.00228:1(82-97)Online publication date: 1-Jan-2016
https://dl.acm.org/doi/10.1016/j.jksuci.2015.05.002
Cao PDong ZLiu KCai K(2013)Quantitative effects of software testing on reliability improvement in the presence of imperfect debuggingInformation Sciences: an International Journal10.1016/j.ins.2012.06.034218(119-132)Online publication date: 1-Jan-2013
https://dl.acm.org/doi/10.1016/j.ins.2012.06.034
Torres-Jimenez JRodriguez-Tello E(2012)New bounds for binary covering arrays using simulated annealingInformation Sciences: an International Journal10.1016/j.ins.2011.09.020185:1(137-152)Online publication date: 1-Feb-2012
https://dl.acm.org/doi/10.1016/j.ins.2011.09.020
Zamli KKlaib MYounis MIsa NAbdullah R(2011)Design and implementation of a t-way test data generation strategy with automated execution tool supportInformation Sciences: an International Journal10.1016/j.ins.2011.01.002181:9(1741-1758)Online publication date: 1-May-2011
https://dl.acm.org/doi/10.1016/j.ins.2011.01.002

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