article

Optimal testing-resource allocation with genetic algorithm for modular software systems

Authors:

Y. S. Dai,

M. Xie,

K. L. Poh,

B. YangAuthors Info & Claims

Journal of Systems and Software, Volume 66, Issue 1

Pages 47 - 55

https://doi.org/10.1016/S0164-1212(02)00062-6

Published: 15 April 2003 Publication History

Abstract

In software testing, an important issue is to allocate the limited testing resources to achieve maximum reliability. There are numerous publications on this issue, but the models are usually developed under the assumption of simple series or parallel modules. For complex system configuration, the optimization problem becomes difficult to solve. In this paper, we present a genetic algorithm for testing-resource allocation problems that can be used when the software systems structure is complex, and also when there are multiple objectives. We consider both system reliability and testing cost in the testing-resource allocation problems. The approach is easily implemented. Some numerical examples are shown to illustrate the applicability of the approach.

References

[1]

Coit, D.W., 1998. Economic allocation of test times for subsystem-level reliability growth testing. IIE Trans. 30 (12), 1143-1151.

Crossref

Google Scholar

[2]

Coit, D.W., Smith, A.E., 1996. Reliability optimization of series-parallel systems using a genetic algorithm. IEEE Trans. Reliab. 45 (2), 254-266.

Crossref

Google Scholar

[3]

Czuchra, W., 1999. Optimizing budget spendings for software implementation and testing. Comput. Operat. Res. 26 (7), 731-747.

Digital Library

Google Scholar

[4]

Goel, A.L., Okumoto, K., 1979. Time dependent error-detection rate model for software reliability and other performance measures. IEEE Trans. Reliab. R-28, 206-211.

Crossref

Google Scholar

[5]

Goldberg, D.E., 1989. Genetic Algorithms in Search of Optimization and Machine Learning. Addison-Wesley.

Crossref

Google Scholar

[6]

Helander, M.E., Zhao, M., Ohlsson, N., 1998. Planning models for software reliability and cost. IEEE Trans. Software Eng. 24 (6), 420-434.

Digital Library

Google Scholar

[7]

Hou, R.H., Kuo, S.Y., Chang, Y.P., 1996. Needed resources for software module test using the hyper-geometric software reliability growth model. IEEE Trans. Reliab. 45 (4), 541-549.

Crossref

Google Scholar

[8]

Knight, J.C., Leveson, N.G., 1986. An experimental evaluation of the assumption of independence in multiversion programming. IEEE Trans. Software Eng. SE-12 (1), 96-109.

Crossref

Google Scholar

[9]

Kumar, A., Malik, K., 1991. Voting mechanisms in distributed systems. IEEE Trans. Reliab. 40 (5), 593-600.

Crossref

Google Scholar

[10]

Kuo, W., Prasad, V.R., 2000. An annotated overview of systemreliability optimisation. IEEE Trans. Reliab. 49 (2), 176-187.

Crossref

Google Scholar

[11]

Leung, Y.W., 1997a. Dynamic resource-allocation for software-module testing. J. Syst. Software 37 (2), 129-139.

Digital Library

Google Scholar

[12]

Leung, Y.W., 1997b. Software reliability allocation under an uncertain operational profile. J. Operat. Res. Soc. 48 (4), 401-411.

Crossref

Google Scholar

[13]

Levitin, G., Lisnianski, A., 2001. A new approach to solving problems of multi-state system reliability optimisation. Quality Reliab. Eng. Int. 17 (2), 93-104.

Crossref

Google Scholar

[14]

Littlewood, B., 1979. How to measure software reliability and how not to. IEEE Trans. Reliab. R-28 (2), 103-110.

Crossref

Google Scholar

[15]

Ohtera, H., Yamada, S., 1990. Optimal allocation and control problems for software-testing resources. IEEE Trans. Reliab. 39 (2), 171-176.

Crossref

Google Scholar

[16]

Painton, L., Campbell, J., 1995. Genetic algorithms in optimization of system reliability. IEEE Trans. Reliab. 44 (2), 172-178.

Crossref

Google Scholar

[17]

Tian, J., 1999. Measurement and continuous improvement of software reliability throughout software life-cycle. J. Syst. Software 47 (2-3), 189-195.

Digital Library

Google Scholar

[18]

Tom, P.A., Murthy, C.S.R., 1998. Algorithms for reliability-oriented module allocation in distributed computing systems. J. Syst. Software 40 (2), 125-138.

Digital Library

Google Scholar

[19]

Wu, J., Fernandez, E.B., Zhang, M.X., 1996. Design and modeling of hybrid fault-tolerant software with cost constraints. J. Syst. Software 35 (2), 141-149.

Digital Library

Google Scholar

[20]

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

Google Scholar

[21]

Yamada, S.T., Nishiwaki, I.M., 1995. Optimal allocation policies for testing-resource based on a software reliability growth model. Math. Comput. Model. 22 (10-12), 295-301.

Digital Library

Google Scholar

[22]

Yang, B., Xie, M., 2000. A study of operational and testing reliability in software reliability analysis. Reliab. Eng. Syst. Safety 70, 323-329.

Crossref

Google Scholar

[23]

Yang, B., Xie, M., 2001. Optimal testing-time allocation for modular systems. Int. J. Quality Reliab. Manage. 18 (8), 854-863.

Crossref

Google Scholar

[24]

Yang, J.E., Hwang, M.J., Sung, T.Y., Jin, Y., 1999. Application of genetic algorithm for reliability allocation in nuclear power plants. Reliab. Eng. Syst. Safety 65 (3), 229-238.

Crossref

Google Scholar

[25]

Zaki, M., El-Ramsisi, A., Omran, R., 2000. A soft computing approach for recognition of occluded shapes. J. Syst. Software 51 (1), 73-83.

Digital Library

Google Scholar

[26]

Zhang, X.M., Shin, M.Y., Pham, H., 2001. Exploratory analysis of environmental factors for enhancing the software reliability assessment. J. Syst. Software 57 (1), 73-78.

Crossref

Google Scholar

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Su ZZhang GYue FZhan DLi MLi BYao X(2021)Enhanced Constraint Handling for Reliability-Constrained Multiobjective Testing Resource AllocationIEEE Transactions on Evolutionary Computation10.1109/TEVC.2021.305553825:3(537-551)Online publication date: 1-Jun-2021
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Wang CJin ZZhang WZowghi DZhao HJiao W(2021)Activity Diagram Synthesis Using Labelled Graphs and the Genetic AlgorithmJournal of Computer Science and Technology10.1007/s11390-020-0293-936:6(1388-1406)Online publication date: 1-Dec-2021
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Shen YJiang YXu CYu PMa XLu JHuchard MKästner CFraser G(2018)ReScue: crafting regular expression DoS attacksProceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering10.1145/3238147.3238159(225-235)Online publication date: 3-Sep-2018
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Index Terms

Optimal testing-resource allocation with genetic algorithm for modular software systems
1. Social and professional topics
  1. Professional topics
    1. Management of computing and information systems
2. Software and its engineering
  1. Software creation and management
    1. Software development process management
    2. Software verification and validation
      1. Software defect analysis
        Software testing and debugging
  2. Software notations and tools
    1. General programming languages
      1. Language features
        Modules / packages

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Reviews

Reviewer: Frank Pospiech

As the authors note in their abstract, "an important issue [in software testing] is to allocate the limited testing resources to achieve maximum reliability." They derive an algorithm, called the genetic algorithm, that promises to help solve this issue for complex software systems. Some examples compare their approach with classical methods, which usually concentrate on reliability, without regard to testing cost. Although the authors' idea has some important limitations (it neglects any mutual interdependence in failures between the software modules), it provides some very useful progress in planning the test phase of big software projects in a cost efficient way, thereby preserving control of test goals such as reliability. Hence, the paper is of benefit to anyone who is involved in test planning of large software projects. Online Computing Reviews Service

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cover image Journal of Systems and Software

Journal of Systems and Software Volume 66, Issue 1

15 April 2003

85 pages

ISSN:0164-1212

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Publisher

Elsevier Science Inc.

United States

Publication History

Published: 15 April 2003

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Su ZZhang GYue FZhan DLi MLi BYao X(2021)Enhanced Constraint Handling for Reliability-Constrained Multiobjective Testing Resource AllocationIEEE Transactions on Evolutionary Computation10.1109/TEVC.2021.305553825:3(537-551)Online publication date: 1-Jun-2021
https://dl.acm.org/doi/10.1109/TEVC.2021.3055538
Wang CJin ZZhang WZowghi DZhao HJiao W(2021)Activity Diagram Synthesis Using Labelled Graphs and the Genetic AlgorithmJournal of Computer Science and Technology10.1007/s11390-020-0293-936:6(1388-1406)Online publication date: 1-Dec-2021
https://dl.acm.org/doi/10.1007/s11390-020-0293-9
Shen YJiang YXu CYu PMa XLu JHuchard MKästner CFraser G(2018)ReScue: crafting regular expression DoS attacksProceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering10.1145/3238147.3238159(225-235)Online publication date: 3-Sep-2018
https://dl.acm.org/doi/10.1145/3238147.3238159
Pietrantuono RRusso SGaleotti JGorla A(2018)Search-based optimization for the testing resource allocation problemProceedings of the 11th International Workshop on Search-Based Software Testing10.1145/3194718.3194721(6-12)Online publication date: 28-May-2018
https://dl.acm.org/doi/10.1145/3194718.3194721
Fan KYou WLi Y(2013)An effective modified binary particle swarm optimization (mBPSO) algorithm for multi-objective resource allocation problem (MORAP)Applied Mathematics and Computation10.5555/2745046.2745223221:C(257-267)Online publication date: 15-Sep-2013
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Zhang XLin MZhang DMei HLv JWang QLiu L(2012)A learning strategy for software testing optimization based on dynamic programmingProceedings of the Fourth Asia-Pacific Symposium on Internetware10.1145/2430475.2430483(1-6)Online publication date: 30-Oct-2012
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Lin CGen M(2008)Multi-criteria human resource allocation for solving multistage combinatorial optimization problems using multiobjective hybrid genetic algorithmExpert Systems with Applications: An International Journal10.1016/j.eswa.2007.04.01634:4(2480-2490)Online publication date: 1-May-2008
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Dai YXie MLong QNg S(2007)Uncertainty Analysis in Software Reliability Modeling by Bayesian Analysis with Maximum-Entropy PrincipleIEEE Transactions on Software Engineering10.1109/TSE.2007.7073933:11(781-795)Online publication date: 1-Nov-2007
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Lin CGen M(2007)Multiobjective resource allocation problem by multistage decision-based hybrid genetic algorithmApplied Mathematics and Computation10.1016/j.amc.2006.08.170187:2(574-583)Online publication date: 1-Apr-2007
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Huang CLo J(2006)Optimal resource allocation for cost and reliability of modular software systems in the testing phaseJournal of Systems and Software10.1016/j.jss.2005.06.03979:5(653-664)Online publication date: 1-May-2006
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Optimal allocation policies for testing-resource based on a software reliability growth model

Optimal resource allocation for cost and reliability of modular software systems in the testing phase

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