research-article

Cloud-based automatic test data generation framework

Authors:

Priyanka Chawla,

Inderveer Chana,

Ajay RanaAuthors Info & Claims

Journal of Computer and System Sciences, Volume 82, Issue 5

Pages 712 - 738

https://doi.org/10.1016/j.jcss.2015.12.001

Published: 01 August 2016 Publication History

Abstract

Designing test cases is one of the most crucial activities in software testing process. Manual test case design might result in inadequate testing outputs due to lack of expertise and/or skill requirements. This article delivers automatic test data generation framework by effectively utilizing soft computing technique with Apache Hadoop MapReduce as the parallelization framework. We have evaluated and analyzed statistically our proposed framework using real world open source libraries. The experimental results conducted on Hadoop cluster with ten nodes are effective and our framework significantly outperforms other existing cloud-based testing models. Proposed the framework for effective cloud-based testing.Designed and developed Hadoop MapReduce based automated test data generation strategy using GA and PSO.Devised and implemented the new approach for the gbest evaluation using Pareto-optimality.Empirical evaluation of the proposed framework.Comparison with the other existing soft-computing based cloud testing models.

References

[1]

Shaukat Ali, Lionel C. Briand, Hadi Hemmati, Rajwinder Kaur Panesar-Walawege, A systematic review of the application and empirical investigation of search-based test case generation, IEEE Trans. Softw. Eng., 36 (November 2010) 742-762.

Digital Library

[2]

I. Aljarah, S.A. Ludwig, Parallel particle swarm optimization clustering algorithm based on MapReduce methodology, in: 2012 Fourth World Congress on Nature and Biologically Inspired Computing, 5-9 Nov. 2012, pp. 104-111.

[3]

Apache Hadoop map reduce. http://www.hadoop.apache.org/mapreduce

[4]

A. Arcuri, L. Briand, A practical guide for using statistical tests to assess randomized algorithms in software engineering, in: 2011 33rd International Conference on Software Engineering, 21-28 May 2011, pp. 1-10.

Digital Library

[5]

T. Banzai, H. Koizumi, R. Kanbayashi, T. Imada, T. Hanawa, M. Sato, D-Cloud: design of a software testing environment for reliable distributed systems using cloud computing technology, in: Proceedings of the 2010 10th IEEE ACM International Conference on Cluster, Cloud and Grid Computing, IEEE Computer Society, Washington, DC, USA, 2010, pp. 631-636.

Digital Library

[6]

S. Bucur, V. Ureche, C. Zamfir, G. Candea, Parallel symbolic execution for automated real-world software testing, in: Proceedings of the Sixth Conference on Computer Systems, ACM, New York, NY, USA, 2011, pp. 183-198.

Digital Library

[7]

Jeremy Booher, Computability: Turing machines and the Halting problem, 2008.

[8]

. http://c4j-team.github.io/C4J/index.html

[9]

Inderveer Chana, Priyanka Chawla, Testing perspectives of cloud based applications, in: Software Engineering Frameworks for Cloud Computing Paradigm, Springer, 2013. http://www.springer.com/computer/communication+networks/book/978-1-4471-5030-5

[10]

Xiang Chen, Qing Gu, Jingxian Qi, Daoxu Chen, Applying particle swarm optimization to pairwise testing, in: 2010 IEEE 34th Annual Conference on Computer Software and Applications Conference, 19-23 July 2010, pp. 107-116.

Digital Library

[11]

Liviu Ciortea, Cristian Zamfir, Stefan Bucur, Vitaly Chipounov, George Candea, Cloud9: a software testing service, Oper. Syst. Rev., 43 (January 2010) 5-10.

Digital Library

[12]

J. Clark, J.J. Dolado, M. Harman, R. Hierons, B. Jones, M. Lumkin, B. Mitchell, S. Mancoridis, K. Rees, M. Roper, M. Shepperd, Reformulating software engineering as a search problem, IEE Proc., Softw., 150 (2003) 161-175.

[13]

Cognizant, Taking testing to the cloud, in: Cognizant Whitepaper, September 2011. http://www.cognizant.com/Taking-Testing-to-the-Cloud.pdf

[14]

Jeffrey Dean, Sanjay Ghemawat, MapReduce: simplified data processing on large clusters, Commun. ACM, 51 (January 2008) 107-113.

Digital Library

[15]

K. Deb, L. Thiele, M. Laumanns, E. Zitzler, Scalable multiobjective optimization test problems, in: Proc. of Congress on Evolutionary Computation, 2002.

[16]

K. Deb, A. Pratap, S. Agarwal, T. Meyarivan, A fast and elitist multiobjective genetic algorithm: NSGA-II, IEEE Trans. Evol. Comput., 6 (Apr. 2002) 182-197.

Digital Library

[17]

Linda Di Geronimo, Filomena Ferrucci, Alfonso Murolo, Federica Sarro, A parallel genetic algorithm based on Hadoop MapReduce for the automatic generation of JUnit test suites, in: Proceedings of the 2012 IEEE Fifth International Conference on Software Testing, Verification and Validation, IEEE Computer Society, Washington, DC, USA, 2012, pp. 785-793.

Digital Library

[18]

S. Di Martino, F. Ferrucci, V. Maggio, F. Sarro, Towards migrating genetic algorithms for test data generation to the cloud, in: IGI Global, 2012, pp. 113-135.

[19]

R.N. Duarte, W. Cirne, F. Brasileiro, P. Duarte, D.L. Machado, Using the computational grid to speed up software testing, in: Proceedings of 19th Brazilian Symposium on Software Engineering, 2005.

[20]

A. Duarte, W. Cirne, F. Brasileiro, P. Machado, Gridunit: software testing on the grid, in: Proceedings of the 28th International Conference on Software Engineering, 2006, pp. 779-782.

[21]

A. Duarte, W. Cirne, F. Brasileiro, P. Machado, Gridunit: software testing on the grid, in: ICSE'06: Proceedings of the 28th International Conference on Software Engineering, ACM, New York, NY, USA, 2006, pp. 779-782.

Digital Library

[22]

A. Duarte, G. Wagner, F. Brasileiro, W. Cirne, Multienvironment software testing on the grid, in: Proceedings of the 2006 Workshop on Parallel and Distributed Systems: Testing and Debugging, 2006, pp. 61-68.

[23]

A. Duarte, G. Wagner, F. Brasileiro, W. Cirne, Multienvironment software testing on the grid, in: PADTAD '06: Proceedings of the 2006 Workshop on Parallel and Distributed Systems: Testing and Debugging, ACM, New York, NY, USA, 2006, pp. 61-68.

[24]

G. Fraser, A. Arcuri, Evolutionary generation of whole test suites, in: 2011 11th International Conference on Quality Software, 13-14 July 2011, pp. 31-40.

Digital Library

[25]

Fujitsu, Confidence in Cloud Grows, Paving Way for New Levels of Business Efficiency, Fujitsu Press Release, November 2010. http://www.fujitsu.com/uk/news/

[26]

S. Gaisbauer, J. Kirschnick, N. Edwards, J. Rolia, VATS: virtualized-aware automated test service, in: Fifth International Conference on Quantitative Evaluation of Systems, September 2008, pp. 93-102.

Digital Library

[27]

Z. Ganon, I.E. Zilbershtein, Cloud-based performance testing of network management systems, in: IEEE 14th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks 2009, IEEE, 2009, pp. 1-6.

[28]

David E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA, 1989.

[29]

Mark Harman, The current state and future of search based software engineering, in: 2007 Future of Software Engineering, IEEE Computer Society, Washington, DC, USA, 2007, pp. 342-357.

[30]

M. Harman, B. Jones, Search-based software engineering, Inf. Softw. Technol., 43 (2001) 833-839.

[31]

M. Harman, K. Lakhotia, P. McMinn, A multi-objective approach to search-based test data generation, in: Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation, ACM, London, England, 2007, pp. 1098-1105.

[32]

. http://www.eclemma.org/jacoco/

[33]

. http://www.ic.unicamp.br/~eliane/JACA.html

[34]

C. Jin, C. Vecchiola, R. Buyya, MRPGA: an extension of MapReduce for parallelizing genetic algorithms, eScience (2008) 214-221.

[35]

B.F. Jones, H. Sthamer, D.E. Eyres, Automatic test data generation using genetic algorithms, Softw. Eng. J., 11 (1996) 299-306.

[36]

G.M. Kapfhammer, Automatically and transparently distributing the execution of regression test suites, in: Proceedings of the 18th International Conference on Testing Computer Software, 2000.

[37]

James Kennedy, Russell C. Eberhart, Particle swarm optimization, in: IEEE Service Center. Learning, Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA, 1995, pp. 1942-1948.

[38]

A. Lastovetsky, Parallel testing of distributed software, Inf. Softw. Technol., 47 (2005) 657-662.

Digital Library

[39]

Alexey Lastovetsky, Parallel testing of distributed software, Inf. Softw. Technol., 47 (July 2005) 657-662.

Digital Library

[40]

Aiguo Li, Yanli Zhang, Automatic generating all-path test data of a program based on PSO, in: Proceedings of the 2009 WRI World Congress on Software Engineering, vol. 04, IEEE Computer Society, Washington, DC, USA, 2009, pp. 189-193.

Digital Library

[41]

Y. Li, T. Dong, X. Zhang, Y. duan Song, X. Yuan, Largescale software unit testing on the grid, May 2006, pp. 596-599.

[42]

Phil McMinn, Search-based software test data generation: a survey: research articles, Softw. Test. Verif. Reliab., 14 (June 2004) 105-156.

Digital Library

[43]

A.W. McNabb, C.K. Monson, K.D. Seppi, Parallel PSO using MapReduce, in: 2007 IEEE Congress on Evolutionary Computation, 25-28 Sept. 2007, pp. 7-14.

[44]

S. Misailovic, A. Milicevic, N. Petrovic, S. Khurshid, D. Marinov, Parallel test generation and execution with Korat, in: Proceedings of the 6th Joint Meeting of the European Software Engineering Conference and the ACM SIGSOFT Symposium on the Foundations of Software Engineering, 2007, pp. 135-144.

Digital Library

[45]

M. Oriol, F. Ullah, Yeti on the cloud, in: 2010 Third International Conference on Software Testing, Verification, and Validation Workshops, April 2010, pp. 434-437.

Digital Library

[46]

R.P. Pargas, M.J. Harrold, R.R. Peck, Test-data generation using genetic algorithms, Softw. Test. Verif. Reliab., 9 (1999) 263-282.

[47]

T. Parveen, S. Tilley, N. Daley, P. Morales, Towards a distributed execution framework for JUnit test cases, in: IEEE International Conference on Software Maintenance, 2009, 20-26 Sept. 2009, pp. 425-428.

[48]

D.A. Patterson, Technical perspective: the data center is the computer, Commun. ACM, 51 (January 2008) 105.

Digital Library

[49]

Priyanka, Inderveer Chana, Ajay Rana, Empirical evaluation of cloud-based testing techniques: a systematic review, Softw. Eng. Notes, 37 (May 2012) 1-9.

[50]

Priyanka, Inderveer Chana, Ajay Rana, A novel strategy for automatic test data generation using soft computing technique J, Front. Comput. Sci., 9 (2015) 346-363.

Digital Library

[51]

P. Rabanal, I. Rodríguez, F. Rubio, A functional approach to parallelize particle swarm optimization, in: Metaheurísticas, Algoritmos Evolutivos y Bioinspirados, MAEB'12, 2012.

[52]

G. Rothermel, R. Untch, C. Chu, M.J. Harrold, Prioritizing test cases for regression testing, IEEE Trans. Softw. Eng., 27 (Oct. 2001) 929-948.

Digital Library

[53]

E. Starkloff, Designing a parallel, distributed test system, IEEE Aerosp. Electron. Syst. Mag., 16 (Jun. 2001) 3-6.

[54]

P. Tonella, Evolutionary testing of classes, in: Proceedings of the International Symposium on Software Testing and Analysis, 2004, pp. 119-128.

Digital Library

[55]

A. Verma, X. Llorà, D.E. Goldberg, R.H. Campbell, Scaling genetic algorithms using MapReduce, in: Proceedings of the 2009 Ninth International Conference on Intelligent Systems Design and Applications, IEEE Computer Society, Washington, DC, USA, 2009, pp. 13-18.

Digital Library

[56]

J. Wegener, A. Baresel, H. Sthamer, Evolutionary test environment for automatic structural testing, Inf. Softw. Technol., 43 (2001) 841-854.

[57]

Andreas Windisch, Stefan Wappler, Joachim Wegener, Applying particle swarm optimization to software testing, in: Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation, ACM, New York, NY, USA, 2007, pp. 1121-1128.

[58]

A. Windisch, S. Wappler, J. Wegener, Applying particle swarm optimization to software testing, in: Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation, 2007, pp. 1121-1128.

[59]

S.E. Xanthakis, C.C. Skourlas, A.K. LeGall, Application of genetic algorithms to software testing, in: Proceedings of the 5th International Conference on Software Engineering and Its Applications, 1992, pp. 625-636.

[60]

Shin Yoo, Mark Harman, Using hybrid algorithm for Pareto efficient multi-objective test suite minimisation, J. Syst. Softw., 83 (April 2010) 689-701.

Digital Library

[61]

L. Yu, W. Tsai, X. Chen, L. Liu, Y. Zhao, L. Tang, W. Zhao, Testing as a service over cloud, in: 2010 Fifth IEEE International Symposium on Service Oriented System Engineering, 2010, pp. 181-188.

Digital Library

[62]

Sheng Zhang, Ying Zhang, Hong Zhou, Qingquan He, Automatic path test data generation based on GA-PSO, in: 2010 IEEE International Conference on Intelligent Computing and Intelligent Systems, vol. 1, 29-31 Oct. 2010, pp. 142-146.

Cited By

Khan HAli FNazir S(2024)Systematic analysis of software development in cloud computing perceptionsJournal of Software: Evolution and Process10.1002/smr.248536:2Online publication date: 13-Feb-2024
https://dl.acm.org/doi/10.1002/smr.2485
Ali SBaseer SAbbasi IAlouffi BAlosaimi WHuang J(2022)Analyzing the interactions among factors affecting cloud adoption for software testing: a two-stage ISM-ANN approachSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-022-07062-326:16(8047-8075)Online publication date: 1-Aug-2022
https://dl.acm.org/doi/10.1007/s00500-022-07062-3
Nooraei Abadeh M(2021)Genetic-based web regression testing: an ontology-based multi-objective evolutionary framework to auto-regression testing of web applicationsService Oriented Computing and Applications10.1007/s11761-020-00312-y15:1(55-74)Online publication date: 1-Mar-2021
https://dl.acm.org/doi/10.1007/s11761-020-00312-y
Show More Cited By

Index Terms

Cloud-based automatic test data generation framework
1. Software and its engineering
  1. Software creation and management
    1. Software verification and validation
      1. Software defect analysis
        Software testing and debugging

Index terms have been assigned to the content through auto-classification.

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Published In

cover image Journal of Computer and System Sciences

Journal of Computer and System Sciences Volume 82, Issue 5

August 2016

333 pages

ISSN:0022-0000

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Copyright © Elsevier Inc.

Publisher

Academic Press, Inc.

United States

Publication History

Published: 01 August 2016

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Cited By

Khan HAli FNazir S(2024)Systematic analysis of software development in cloud computing perceptionsJournal of Software: Evolution and Process10.1002/smr.248536:2Online publication date: 13-Feb-2024
https://dl.acm.org/doi/10.1002/smr.2485
Ali SBaseer SAbbasi IAlouffi BAlosaimi WHuang J(2022)Analyzing the interactions among factors affecting cloud adoption for software testing: a two-stage ISM-ANN approachSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-022-07062-326:16(8047-8075)Online publication date: 1-Aug-2022
https://dl.acm.org/doi/10.1007/s00500-022-07062-3
Nooraei Abadeh M(2021)Genetic-based web regression testing: an ontology-based multi-objective evolutionary framework to auto-regression testing of web applicationsService Oriented Computing and Applications10.1007/s11761-020-00312-y15:1(55-74)Online publication date: 1-Mar-2021
https://dl.acm.org/doi/10.1007/s11761-020-00312-y
Bertolino AAngelis GGallego MGarcía BGortázar FLonetti FMarchetti E(2019)A Systematic Review on Cloud TestingACM Computing Surveys10.1145/333144752:5(1-42)Online publication date: 13-Sep-2019
https://dl.acm.org/doi/10.1145/3331447
Li PDong LXu HLau T(2018)Spark’s operation time predictive in cloud computing environment based on SRC-WSVRJournal of High Speed Networks10.3233/JHS-17058024:1(49-62)Online publication date: 1-Jan-2018
https://dl.acm.org/doi/10.3233/JHS-170580

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