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Extraction of non-functional requirement using semantic similarity distance

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Abstract

Functional and non-functional requirements are important equally in software development. Usually, the requirements are expressed in natural languages. The functional and non-functional requirements are written inter-mixed in software requirement document. The extraction of requirement from the software requirement document is a challenging task. Most of the recent studies adopted a supervised learning approach for the extraction of non-functional requirements. However, there is a drawback of supervised learning such as training of model and retrain if the domain changed. The proposed approach manipulates the textual semantic of functional requirements to identify the non-functional requirements. The semantic similarity is calculated based on co-occurrence of patterns in large human knowledge repositories of Wikipedia. This study finds the similarity distance between the popular indicator keywords and requirement statements to identify the type of non-functional requirement. The proposed approach is applied to PROMISE “NFR dataset.” The performance of the proposed approach is measured in terms of precision, recall and F-measure. Furthermore, the research applies three pre-processing approaches (traditional, part of speech tagging and word augmentation) to increase the performance of NFR extraction. The proposed approach outperforms the results of existing studies.

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Notes

  1. https://cloud.google.com/vision/.

  2. https://cloud.google.com/speech/.

  3. https://cchit.org.

  4. https://github.com/RealsearchGroup/NFRLocator.

  5. https://code.google.com/archive/p/word2vec/.

  6. https://groups.google.com/forum/#!topic/gensim/MJWrDw_IvXw.

  7. https://github.com/idio/wiki2vec.

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Acknowledgements

We are thankful to the Ministry of Science, Technology and Innovation (MOSTI) to support this research under eScience grant vote: 4S113 and UTM-TDR grant vote: 06G23.

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Authors and Affiliations

  1. Software Engineering Department, School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, Johor Bahru, Malaysia

    Muhammad Younas, D. N. A. Jawawi & Muhammad Arif Shah

  2. Department of Computer Science, Government College University Faisalabad, Faisalabad, Pakistan

    Muhammad Younas

  3. Department of Computer Science, Indiana University of Pennsylvania, Indiana, PA, 15705, USA

    Imran Ghani

  4. City University of Sciences and Information Technology, Peshawar, Pakistan

    Muhammad Arif Shah

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  1. Muhammad Younas
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Correspondence to Muhammad Younas.

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Younas, M., Jawawi, D.N.A., Ghani, I. et al. Extraction of non-functional requirement using semantic similarity distance. Neural Comput & Applic 32, 7383–7397 (2020). https://doi.org/10.1007/s00521-019-04226-5

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