Req2Test - Graph Driven Test Case Generation for Domain Specific Requirement
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International Journal of Computer Trends and Technology (IJCTT) |  |
| © 2018 by IJCTT Journal | ||
| Volume-60 Number-2 |
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| Year of Publication : 2018 | ||
| Authors : Veera Prathap Reddy M, Prasad P.V.R.D, Manjunath Chikkamath, Karthikeyan Ponnalagu, Sarathchandra Mandadi, and Praveen C.V.R | ||
| DOI : 10.14445/22312803/IJCTT-V60P120 |
Veera Prathap Reddy M, Prasad P.V.R.D, Manjunath Chikkamath, Karthikeyan Ponnalagu, Sarathchandra Mandadi, and Praveen C.V.R "Req2Test - Graph Driven Test Case Generation for Domain Specific Requirement". International Journal of Computer Trends and Technology (IJCTT) V60(2):123-132 June 2018. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.
Abstract
Software testing is a critical phase in the software development life cycle, as it validates the software against its requirements. Auto generation of test cases for software testing from natural language requirements pose a formidable challenge as requirements often do not follow a defined structure. In this paper, we propose Req2Test pipeline to auto generate test cases from a set of requirement statements. Our process includes domain specific knowledge graphs for extracting information, domain ontologies for identifying hierarchy of domain components and action sequences for actions to be performed in achieving a task. Knowledge graphs, domain ontology and action sequences contributes in addressing complete test coverage for requirement statements. The test cases are generated against industrial requirement statements on Automatic Wiper Control System in Automotive Domain and achieved promising results. We provide experimental results on industrial requirement and discuss the advantages and shortcomings of our approach.
Reference
[1] Charles P. Morgan, Mark l. Gillenson, Xihui Zhang, Son N. Bui and Euntae ?TED? Lee, ?ATCG: An Automated Test Case Generator?,Journal of Information Technology Management, vol. XXVII, no. 3, pp.112-120, 2016.
[2] Priyanka Kulkarni and Yashada Joglekar, ?Generating and analyzing test cases from software requirements using NLP and Hadoop?, International Journal of Current Engineering and Technology, vol. 4, no.6, pp.3934-3937, 2014.
[3] Anurag Dwarakanathand Shubhashis Sengupta, ?Litmus: Generation of test cases from functional requirements in natural language? in Proceedings of NLDB`12 Proceedings of the 17th international conference on Applications of Natural Language Processing and Information Systems, pp.58-69, 2012.
[4] Mich Luisa, Franch Mariangela and Novi Inverardi Pierluigi, ?Market research for requirements analysis using linguistic tools?,Requirements Eng, pp.40-56, 2004.
[5] Colin JNeill and Phillip A Laplante, ?Requirements engineering: the state of the practice?,in IEEE Software, vol. 20, no. 6, pp. 40-45, 2003.
[6] Kirti Nagpal and Raman Chawla, ?Improvement of Software Development Process: A new SDLC Model?, International Journal of Latest Research in Science and Technology, vol.1, no.3, pp. 217-224,2012.
[7] N Vijay,?Little Joe Model of Software Testing?, Software Solutions Lab, Honeywell, Bangalore, India, pp.1-12, 2001.
[8] Elaine Weyuker, Tar& Goradia, and Ashutosh Singh, ?Automatically Generating Test Data from a Boolean Specification?, IEEE Transactions on Software Engineering, vol 20, no. 5, pp. 353-363, 1994.
[9] ?Requirements Based Testing Process Overview?, Bender RBT Inc. Queensbury, New York, USA, pp. 1-18, 2009.
[10] James Martin, An information systems manifesto, Prentice Hall, p.300, 1984.
[11] L. H. Tahat, B. Vaysburg, B. Korel and A. J. Bader, "Requirement-based automated black-box test generation," in25th Annual International Computer Software and Applications Conference. COMPSAC 2001, Chicago, IL, pp. 489-495, 2001.
[12] Prabakaran P.M. (2012) ?How automatic wiper control works in modernCar?,[Online].Available:https://www.engineersgarage.com/contribution/how-automatic-wiper-control-works-modern-car
[13] Matthew Honnibal and Mark Johnson, ?An improved non-monotonic transition system for dependency parsing?, in Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing,Lisbon, Portugal,pp. 1373–1378, 2015.
[14] Google‘s new artificial intelligence can‘t understand these sentences. Can you?. Washington Post. Retrieved 2016-12-18.
[15] IEEE Recommended Practice for Software Requirements Specifications,IEEE Std. 830-1998, pp. 1-40, 1998.
[16] R. G Dromey, ?For Requirements to Design: Formalizing the Key Steps?, in Proceedings of First International Conference onSoftware Engineering and Formal Methods, Brisbane, Queensland, Australia, pp. 2-11, 2003.
[17] Cesar Gonzalez, Brian Hendreson-Sellers and Geoff Dromey, ?A Meta model for the Behavior Trees Modeling Technique?, in Proceedings of Third International Conference on Information Technology and Applications (ICITA`05), vol.1, pp. 35-39, 2005.
[18] John R. Anderson, Rules of the Mind,Psychology Press; 1st ed. p. 336, 1993.
[19] Chun-Hung Lu, Shih-Hung Wu, LiongYu Tu and Wen-Lian Hsu, "The design of an intelligent tutoring system based on the ontology of procedural knowledge," in Proceedings of IEEE International Conference on Advanced Learning Technologies, pp. 525-529, 2004.
[20] Zhanfang Zhao, Sung-Kook Han and In-Mi So, ?Architecture of Knowledge Graph Construction Techniques?, International Journal of Pure and Applied Mathematics, vol. 118, no. 19, pp. 1869-1883, 2018.
[21] LeonidKof,Ricardo Gacitua, Mark Rouncefield and Peter Sawyer, ?Ontology and model alignment as a means for requirements validation?, in Proceedings of IEEE Fourth International Conference on Semantic Computing, Pittsburgh, PA, USA,pp.46-51, 2010.
[22] F A Rezaur Rahman Chowdhury, Chao Ma, Md Rakibul Islam, Mohammad Hossein Namaki, Mohammad Omar Faruk and JanardhanRao Doppa, ?Select-and-Evaluate: A Learning Framework for Large- Scale Knowledge Graph Search?, in Proceedings of the Ninth Asian Conference on Machine Learning, vol. 77, pp. 129-144, 2017.
Keywords
Named Entity Recognition, Domain Knowledge, Domain Ontology, Test Case Generation