Advertisement

A NLP Approach to Software Quality Models Evaluation

  • Simona MotognaEmail author
  • Dana Lupsa
  • Ioana Ciuciu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11231)

Abstract

This paper aims to analyze and identify the variations and similarities between models/standards in the software quality domain. The approach combines analysis at several levels, starting with a naive comparison done by the software quality expert, going through several NLP specific similarities measures. The final goal is to be able to rapidly identify solutions to make a software compliant with new standard. The focus of the current study is on the lexical analysis of software quality models based on natural language processing.

Keywords

Software quality NLP Similarity between words 

References

  1. 1.
    ISO 9126-1: Software Engineering - Product Quality (2001). https://www.iso.org/standard/22749.html. Accessed 2015
  2. 2.
    Agirre, E., Cer, D.M., Diab, M.T., Gonzalez-Agirre, A.: SemEval-2012 task 6: a pilot on semantic textual similarity. In: SemEval@NAACL-HLT (2012)Google Scholar
  3. 3.
    Bird, S., Klein, E., Loper, E.: Natural Language Processing with Python, 1st edn. O’Reilly Media Inc., Newton (2009)zbMATHGoogle Scholar
  4. 4.
    Choi, S.S., Cha, S.H.: A survey of binary similarity and distance measures. J. Syst. Cybern. Inform. 8, 43–48 (2010)Google Scholar
  5. 5.
    Ciancarini, P., Nuzzolese, G.A., Presutti, V., Russo, D.: An ontology of software quality relational factors from banking systems. In: Proceedings of the 15th European Semantic Web Conference, ESWC 2018 (2018)Google Scholar
  6. 6.
    Fellbaum, C.: WordNet - An Electronical Lexical Database, vol. 25 (1998)Google Scholar
  7. 7.
    Gomaa, W.H., Fahmy, A.A.: Article: a survey of text similarity approaches. Int. J. Comput. Appl. 68(13), 13–18 (2013). Full text availableGoogle Scholar
  8. 8.
    Harris, Z.: Distributional structure. Word 10(23), 146–162 (1954)CrossRefGoogle Scholar
  9. 9.
    ISO/IEC 25010:2011: Systems and Software Engineering (2011). http://www.iso.org. Accessed 2015
  10. 10.
    Kara, M., Lamouchi, O., Ramdane-Cherif, A.: Ontology software quality model for fuzzy logic evaluation approach. Procedia Comput. Sci. 83, 637–641 (2016). http://www.sciencedirect.com/science/article/pii/S1877050916301739, The 7th International Conference on Ambient Systems, Networks and Technologies (ANT 2016)/The 6th International Conference on Sustainable Energy Information Technology (SEIT-2016)/Affiliated Workshops
  11. 11.
    Kayed, A., Hirzalla, N., Samhan, A.A., Alfayoumi, M.: Towards an ontology for software product quality attributes. In: Proceedings of the 2009 Fourth International Conference on Internet and Web Applications and Services, ICIW 2009, pp. 200–204. IEEE Computer Society, Washington (2009).  https://doi.org/10.1109/ICIW.2009.36
  12. 12.
    McCall, J., Richards, P., Walters, G.: Factors in software quality. Technical report, National Technical Information Service 1 (1977)Google Scholar
  13. 13.
    McInnes, B.T., Pedersen, T.: Improving correlation with human judgments by embedding second order vectors with semantic similarity. CoRR abs/1609.00559 (2016)Google Scholar
  14. 14.
    Mihalcea, R., Corley, C., Strapparava, C.: Corpus-based and knowledge-based measures of text semantic similarity. In: Proceedings of the 21st National Conference on Artificial Intelligence, AAAI 2006, vol. 1, pp. 775–780. AAAI Press (2006)Google Scholar
  15. 15.
    Mikolov, T., Sutskever, I., Chen, K., Corrado, G., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Proceedings of the 26th International Conference on Neural Information Processing Systems, NIPS 2013, vol. 2, pp. 3111–3119. Curran Associates Inc., USA (2013)Google Scholar
  16. 16.
    Resnik, P.: Semantic similarity in a taxonomy: an information-based measure and its application to problems of ambiguity in natural language. J. Artif. Int. Res. 11(1), 95–130 (1999)zbMATHGoogle Scholar
  17. 17.
    Rijsbergen, C.J.V.: Information Retrieval, 2nd edn. Butterworth-Heinemann, Newton (1979)zbMATHGoogle Scholar
  18. 18.
    Yu, Z., Wallace, B.C., Johnson, T.R., Cohen, T.: Retrofitting concept vector representations of medical concepts to improve estimates of semantic similarity and relatedness. CoRR abs/1709.07357 (2017)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Computer Science DepartmentBabes-Bolyai UniversityCluj-NapocaRomania

Personalised recommendations