Detecting Ambiguity in Requirements Analysis Using Mamdani Fuzzy Inference

Authors

  • Jacline Sudah Sinpang Department of Software Engineering, Faculty of Computing, Universiti Teknologi Malaysia, 81310 Johor Bahru, Johor, Malaysia.
  • Shahida Sulaiman Department of Software Engineering, Faculty of Computing, Universiti Teknologi Malaysia, 81310 Johor Bahru, Johor, Malaysia.
  • Norsham Idris Department of Software Engineering, Faculty of Computing, Universiti Teknologi Malaysia, 81310 Johor Bahru, Johor, Malaysia.

Keywords:

Mamdani Fuzzy Inference, Natural Language, Requirements Analysis, Requirements Engineering,

Abstract

Natural language is the most common way to specify requirements during elicitation of requirements as stakeholders can better specify the services they want from a particular system. However, it is arguable that requirements gathered in natural language is free from error especially ambiguity. Ambiguity in requirements can cause requirement engineers or system analysts to perceive the requirements according to their understanding instead of stakeholders understanding. This study attempts to detect ambiguity mainly vagueness as early as possible using Mamdani fuzzy inference when analyzing requirements. Dataset used in this study comprises raw requirements that are still in natural language form. In order to create fuzzy rules, the analysis of the requirements in natural language involves the process of capturing the text patterns of the requirements. The results show that it is possible to use Mamdani fuzzy inference that can detect ambiguity in requirements analysis phase.

References

D. Pandey, U. Suman and A. K. Ramani, “An effective requirement engineering process model for software development and requirements management,” 2010 International Conference on Advances in Recent Technologies in Communication and Computing, Kottayam, 2010, pp. 287-291.

E. Parra, C. Dimou, J. Llorens, V. Moreno and A. Fraga, “A methodology for the classification of quality of requirements using machine learning techniques,” Information and Software Technology, vol. 67, Nov. 2015, pp. 180-195.

ISO/IEC/IEEE International standard, “Systems and software engineering - Life cycle processes - Requirements engineering”, in ISO/IECIEEE 29148:2011(E), pp. 1-94, Dec. 1 2011.

J. Kazmier, B. Berenbach, D. J. Paulish and A. Rudorfer, Software and Systems Requirements Engineering: In Practise. New York, NY: McGraw-Hill Education, 2009, pp. 39-72.

E. Kamsties, “Understanding ambiguity in requirements engineering, Engineering and Managing Software Requirements,” in Engineering and Managing Software Requirements, A. Aurum, and C. Wohlin, Eds. Berlin, Heidelberg: Springer, 2005, pp. 245-266.

S. F. Tjong, Avoiding Ambiguity in Requirements Specifications. Doctoral dissertation, University of Waterloo, 2008.

A. Nigam, N. Arya, B. Nigam, and D. Jain, “Tool for automatic discovery of ambiguity in requirements,” IJCSI International Journal of Computer Science Issues, vol. 9, no. 5, pp. 350-356, Sep. 2012

A. Ferrari, G. Lipari, S. Gnesi and G. O. Spagnolo, “Pragmatic ambiguity detection in natural language requirements,” in 2014 IEEE 1st International Workshop on Artificial Intelligence for Requirements Engineering (AIRE), Karlskrona, 2014, pp. 1-8.

F. Meziane and S. Vadera, “Artificial intelligence in software engineering current developments and future prospects,” in Artificial Intelligence Applications for Improved Software Engineering Development: New Prospects, Hershey, New York, USA: IGI Global, 2010, pp. 273-294.

IEEE/ISO International standard, “Quality management and quality assurance”, in IEEE ISO 8402:1995, 1995.

C. H. Wang, A study of membership functions on Mamdani-type fuzzy inference system for industrial decision-making. Thesis and dissertation, University of Lehigh, 2015.

C. Arora, M. Sabetzadeh, L. Briand and F. Zimmer, “Automated checking of conformance to requirements templates using natural language processing,” in IEEE Transactions on Software Engineering, vol. 41, no. 10, Oct. 1 2015, pp. 944-968,.

B. Gleich, O. Creighton and L. Kof, “Ambiguity detection: towards a tool explaining ambiguity sources,” International Working Conference on Requirements Engineering: Foundation for Software Quality, 2010, pp. 218-232.

A. K. Massey, R. L. Rutledge, A. I. Antón and P. P. Swire, “Identifying and classifying ambiguity for regulatory requirements,” 2014 IEEE 22nd International Requirements Engineering Conference (RE), Karlskrona, 2014, pp. 83-92.

J. M. Davril, M. Cordy, P. Heymans and M Acher, “Using fuzzy modeling for consistent definitions of product qualities in requirements,” 2015 IEEE Second International Workshop on Artificial Intelligence for Requirements Engineering (AIRE), Ottawa, 2015, pp. 1-8.

A. Umber and I. S. Bajwa, “Minimizing ambiguity in natural language software requirements specification,” 2011 Sixth International Conference on Digital Information Management, Melbourn, 2011, pp. 102-107.

S. Bhardwaj and A. K. Goyal, “A comparative analysis of agent oriented requirements engineering frameworks,” International Journal of Computer Applications, vol.87, no.8, Feb. 2014.

M. Kumar, M. Shukla and S. Agarwal, “A hybrid approach of requirements engineering in agile software development,” 2013 International Conference on Machine Intelligence on Research Advancement, Katra, 2013, pp. 515-519.

A. B Siddique, S. Qadri, S. Hussain, S. Ahmad, I. Maqbool, and A. K. N. Khan, “Integration of requirements engineering with UML in software engineering practices,” Sci. Int. (Lahore), vol. 26, no. 5. pp. 2157-2162, 2014.

F. Beritelli, G. Cilia and A. Cucè, “Small vocabulary word recognition based on fuzzy pattern matching,” in Proc. of the European Symposium on Intelligent Techniques, Crete (Greece), 1999.

L. Baresi, L. Pasquale, and P. Spoletini, “Fuzzy goals for requirementsdriven adaptation,” 18th IEEE International Requirements Engineering Conference, Sydney, 2010, pp. 125-134.

S. L. Lim, Social Networks and Collaborative Filtering for LargeScale Requirements Elicitation. Doctoral dissertation, University of New South Wales, 2010.

D. Firesmith, “Specifying good requirements,” Journal of Object Technology, vol. 2, no. 4, pp. 77-87, 2003.

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Published

2017-10-20

How to Cite

Sinpang, J. S., Sulaiman, S., & Idris, N. (2017). Detecting Ambiguity in Requirements Analysis Using Mamdani Fuzzy Inference. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 9(3-4), 157–162. Retrieved from https://jtec.utem.edu.my/jtec/article/view/2936