Data Mining and Knowledge Engineering

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Automatic Requirement Classification:A Comparative Study


Affiliations
1 Department of Information Technology and Systems, Institute of Statistical Studies and Research, Cairo University, Giza, Egypt
     

   Subscribe/Renew Journal


Software requirements must be classified for later use of requirements in the design and implementation phases. This classification can be done manually, which takes a lot of time and effort, or recently it can be done automatically. Automatic requirement classification is an important and promising area in both industry and research. Automatic classification of requirements is implemented using one of machine learning techniques. In this paper, a comparative study is introduced using one of machine learning techniques (Naïve Bayes) in order to observe the accuracy difference between two machine learning tools (Weka and RapidMiner) using a dataset of requirements.


Keywords

Requirement Classification, Non-Functional Requirements, Stemming, Software Projects, Functional Requirements, Weka, RapidMiner.
User
Subscription Login to verify subscription
Notifications
Font Size

  • R. Saranya, "Survey on Security Measures of Software Requirement Engineering," International Journal of Computer Applications, vol. 90, no. 17, p. (0975 – 8887), 2014.

  • P. Zave, "Classification of Research Efforts in Requirements Engineering," ACM Computing Surveys (CSUR) , vol. 29, no. 4, pp. 315-321, 1997.

  • I. S. Boeard, "IEEE Standard Glossary of Software Engineering Terminology," The Institute of Electrical and Electronics Engineers, New York, USA, 1990.

  • M. Glinz, "On Non-Functional Requirements," in 15th IEEE International Requirements Engineering Conference (RE’07), Delhi, India, 2007.

  • Z. S. H. Abad and G. Ruhe, "Using real options to manage Technical Debt in Requirements Engineering," in Requirements Engineering Conference (RE), 2015 IEEE 23rd International, Ottawa, ON, Canada, 2015.

  • R. Feldman and I. Dagan, "Knowledge discovery in Textual Databases (KDT)," in KDD'95 Proceedings of the First International Conference on Knowledge Discovery and Data Mining, Montréal, Québec, Canada, 1995.

  • N. R. D. Haidy H. Mustafa, "Automatic Requirement Classification Technique: Using Different Stemming Algorithms," CiiT International Journal of Data Mining and Knowledge Engineering, vol. 10, no. 6, pp. 122-127, 2018.

  • C. Duan, P. Laurent, J. Cleland-Huang and C. Kwiatkowski, "Towards automated requirements prioritization and triage," Springer-Verlag London Limited, vol. 14, no. 2, p. 73–89, 2009.

  • J. Cleland-Huang, R. Settimi, X. Zou and P. Solc, "Automated classification of non-functional requirements," Springer-Verlag, vol. 12, no. 2, p. 103–120, 2007.

  • E. Knauss and D. Ott, "Automatic requirement categorization of large natural language specifications at mercedes-benz for review improvements," in in Proceedings of the 19th International Conference on Requirements Engineering: Foundation for Software Quality, ser. REFSQ’13., Berlin, Heidelberg: Springer-Verlag, 2013.

  • M. Rahimi, M. Mirakhorli and J. Cleland-Huang, "Automated extraction and visualization of quality concerns from requirements specifications," in 2014 IEEE 22nd International Requirements Engineering Conference (RE) (2014), Karlskrona, Sweden, 2014.

  • Z. Kurtanović and W. Maalej, "Automatically Classifying Functional and Non-functional Requirements Using Supervised Machine Learning," in Requirements Engineering Conference (RE), 2017 IEEE 25th International, Lisbon, Portugal, 2017.

  • E. Knauss, D. Damian, G. Poo-Caamaño and J. Cleland-Huang, "Detecting and classifying patterns of requirements clarifications," in Requirements Engineering Conference (RE), 2012 20th IEEE International, Chicago, IL, USA, 2012.

  • A. Casamayor, D. Godoy and M. Campo, "Identification of non-functional requirements in textual specifications: A semi-supervised learning approach," ELSEVIER, vol. 52, no. 4, pp. 436-445, 2010.

  • Y. joong, S. Park, J. Seo and S. Choib, "Using classification techniques for informal requirements in the requirements analysis-supporting system," ElSEVEIR, vol. 49, no. 11-12, pp. 1128-1140, 2007.

  • A. Fawaz, F. A. Zaghoul, A. Hudaib, A. Alqudah and M. A. M. Abushariah, "A Suggested Framework for Software Requirements Classification," in UKSIM2015, Cambridge UK, 2015.

  • T. K. R. P. D. Menzies, "The Promise Repository of Empirical Software Engineering Data," Department of Computer Science, North Carolina State University, 2016.

  • N. R. D. Haidy H. Mustafa, "Automatic Requirement Classification Technique: Using Different Stemming Algorithms," CiiT International Journal of Data Mining and Knowledge Engineering, vol. 10, no. 6, pp. 122-127, 2018.

  • S. Raschka, Naive Bayes and Text Classification I - Introduction and Theory, arXiv: 1410.5329v4 [cs.LG], 2017.


Abstract Views: 399

PDF Views: 0




  • Automatic Requirement Classification:A Comparative Study

Abstract Views: 399  |  PDF Views: 0

Authors

Haidy H. Mustafa
Department of Information Technology and Systems, Institute of Statistical Studies and Research, Cairo University, Giza, Egypt
Nagy Ramadan Darwish
Department of Information Technology and Systems, Institute of Statistical Studies and Research, Cairo University, Giza, Egypt

Abstract


Software requirements must be classified for later use of requirements in the design and implementation phases. This classification can be done manually, which takes a lot of time and effort, or recently it can be done automatically. Automatic requirement classification is an important and promising area in both industry and research. Automatic classification of requirements is implemented using one of machine learning techniques. In this paper, a comparative study is introduced using one of machine learning techniques (Naïve Bayes) in order to observe the accuracy difference between two machine learning tools (Weka and RapidMiner) using a dataset of requirements.


Keywords


Requirement Classification, Non-Functional Requirements, Stemming, Software Projects, Functional Requirements, Weka, RapidMiner.

References