A Semantic Driven Approach for Requirements Verification

  • Gabriella GiganteEmail author
  • Francesco Gargiulo
  • Massimo Ficco
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 570)


Requirements Engineering (RE) is a key discipline for the success of software projects. Consistency, completeness, and accuracy are the requirements quality properties to be guaranteed by the verification task in RE. An overview of the actual trends in RE is briefly summarized, focusing more closely on the requirements verification quality properties. Completeness results is the most difficult property to guarantee. It is hard to capture the software behavior against the whole external context. In the last years, research has focused its attention to the application of semantic Web techniques to the different tasks of RE. The adoption of ontologies seems promising to achieve the proper level of formalism and to argue on quality properties. This paper presents a survey of the main concepts that need to be accounted for requirement verification, and proposes an ontological engineering approach to demonstrate the overlapping of requirements against the external context.


Requirements Engineering Semantic Driven Approach Ontologies 


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  1. 1.
    SWEBOK Guide to the Software Engineering Body of Knowledge. IEEE Computer Society (2004)Google Scholar
  2. 2.
    Cheng, B., Atlee, J.: Research Directions in Requirement Engineering. In: FOSE 2007, Future of Software Engineering, pp. 285–303 (2007)Google Scholar
  3. 3.
    Fanmuy, G., Fraga, A., Llorens, J.: Requirements verification in the industry. In: Hammami, O., Krob, D., Voirin, J.-L. (eds.) Complex Systems Design & Management, vol. 91, pp. 145–160. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  4. 4.
    Alfor, M., Lawson, J.: Software Requirements Engineering Methodology (Development). TRW Defense and Space Systems Group (1979)Google Scholar
  5. 5.
    Zave, P.: Classification of Research Efforts in Requirements Engineering. ACM Computing Surveys (CSUR) 29(4), 315–321 (1997)CrossRefGoogle Scholar
  6. 6.
    RTCA, DO-178C. Software Consideration. In: Airborne Systems And Equipment Certification, Washington (December 2011)Google Scholar
  7. 7.
    Calero, C., Ruiz, F., Piattini, M.: Ontologies in Software Engineering and Software Technology. Springer (2005)Google Scholar
  8. 8.
    Taye, M.M.: Web-Based Ontology Languages and its Based Description Logics. The Research Bulletin of Jordan ACM II(II), 1–9Google Scholar
  9. 9.
    Zowghi, D., Gervasi, V.: On the Interplay Between Consistency, Completeness, and Correctness. Requirements Evolution, Journal of Information and Software Technology 45 (2003)Google Scholar
  10. 10.
    Nuseibeh, B., Easterbrook, S., Russo, A.: Leveraging Inconsistency in Software Development. Software Development Computer 33(4), 1–33 (2000)Google Scholar
  11. 11.
    Sharma, S., Pandey, S.: Integrating AI techniques in Requirement Phase: A Literature Review. In: IJCA Proceedings on 4th International IT Summit Confluence 2013 - The Next Generation Information Technology Summit Confluence, pp. 21–25 (2013)Google Scholar
  12. 12.
    Zhu, A., Jin, A.: Inconsistency Measurement of Software Requirements Specifications: An Ontology-Based Approach. In: Engineering of Complex Computer Systems, pp. 402–410 (2005)Google Scholar
  13. 13.
    Siegemund, K., Thomas, E., Zhao, Y., Pan, J., Assmann, U.: Towards ontology-driven requirements engineering. In: Workshop Semantic Web Enabled Software Engineering at 10th International Semantic Web Conference (ISWC), pp. 1–6 (2011)Google Scholar
  14. 14.
    Spanoudakis, G., Zisman, A.: Inconsistency Management in Software Engineering: Survey and Open Research Issues. In: Handbook of Software Engineering and Knowledge Engineering, pp. 329–380 (2001)Google Scholar
  15. 15.
    Boehm, B.W.: Verifying and validating software requirements and design specifications. IEEE Software (1), 75–88 (1984)Google Scholar
  16. 16.
    CESAR_D_SP2_R3.3_M3_Vol4_v1.000_PU Project,
  17. 17.
    Castaneda, V., Ballejos, L., Caliusco, M., Galli, M.: The Use of Ontologies in Requirements Engineering. Global Journal of Researches in Engineering 10 (6) (Ver 1.0), 2–7 (2010)Google Scholar
  18. 18.
    Ceccato, M.: Ambiguity Identification and Measurements in Natural Language TextsGoogle Scholar
  19. 19.
    Gasevic, D., Kaviani, N., Milanovi, M.: Ontologies and Software Engineering. In: International Handbooks on Information Systems, pp. 593–615. Springer (2009)Google Scholar
  20. 20.
    Shingler, R., Fadin, G., Umiliacchi, G.P.: From rcm to predictive maintenance: The integrail approach. In: 4th IET International Conference on Railway Condition Monitoring, pp. 1–5 (2008)Google Scholar
  21. 21.
    De Ambrosi, C., Ghersi, C., Tacchella, A.: An ontology-based condition analyzer for fault classification on railway vehicles. In: Chien, B.-C., Hong, T.-P., Chen, S.-M., Ali, M. (eds.) IEA/AIE 2009. LNCS, vol. 5579, pp. 449–458. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  22. 22.
    Lodemann, M., Luttenberger, N.: Ontology-Based Railway Infrastructure Verification. In: Proceeding KMIS 2010, pp. 176–181 (2010)Google Scholar
  23. 23.
    Verstichel, S., Ongenaea, F., Loeve, L., Vermeulen, F., Dings, P., Dhoedt, B., Dhaene, T., De Turck, F.: Efficient data integration in the railway domain through an ontology-based methodology. Transportation Research Part C: Emerging Technologies 19(4), 617–643 (2011)CrossRefGoogle Scholar
  24. 24.
    Kannan, S., Thangavelu, A., Kalivaradhan, R.: An intelligent driver assistance system (idas) for vehicle safety modelling using ontology approach. International Journal of Ubicomp (2010)Google Scholar
  25. 25.
    Lanfranchi, V., Bhagdev, R., Chapman, S., Ciravegna, F., Petrelli, D.: Extracting and Searching Knowledge for the Aerospace Industry. In: ESTC (2007)Google Scholar
  26. 26.
    Bonasso, R., Boddy, M., Kortenkamp, D., Bell, S.: Ontological Models To Support Space OperationsGoogle Scholar
  27. 27.
    Keller, R., Berrios, D., Wolfe, S., Hall, D., Sturken, I.: Semantic Integration of Heterogeneous NASA Mission Data SourcesGoogle Scholar
  28. 28.
    Malin, J., Throop, D.: Basic Concepts and Distinctions for an Aerospace Ontology of Functions, Entities and Problems. In: Aerospace Conference. IEEE (2007)Google Scholar
  29. 29.
    Kuofie, E.J.: RaDEX: A Rationale-based Ontology for Aerospace Design Explanation. Master of Science Programme Business Information Technology University of TwenteGoogle Scholar
  30. 30.
    Verhagen, W., Curran, R.: Ontological Modelling of the Aerospace Composite Manufacturing Domain in Improving Complex Systems Today, pp. 215–222 (2011)Google Scholar
  31. 31.
    Schumann, B., Scanlany, J., Fangohrz, H.: A Generic Unifying Ontology for Civil Un-manned Aerial. In: 12th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference and 14th AIAA/ISSM (2012)Google Scholar
  32. 32.
    Dittmann, L., Rademacher, T., Zelewski, S.: Performing FMEA Using Ontologies. In: 18th International Workshop on Qualitative Reasoning, Evanston, USA, pp. 209–216 (2004)Google Scholar
  33. 33.
    Bogusch, R., Gerlach, S.: Optimierungen in Requirements-Engineering in der PraxisGoogle Scholar
  34. 34.
    Jiang, J.J., Conrath, D.W.: Semantic similarity based on corpus statistics and lexical taxonomy (1997)Google Scholar
  35. 35.
    Leacock, C., Chodorow, M.: Combining local context and WordNet similarity for word sense identification. WordNet: An Electronic Lexical Database 49(2), 265–283 (1998)Google Scholar
  36. 36.
    Lin, D.: An information-theoretic definition of similarity. In: Proceedings of the 15th International Conference on Machine Learning, vol. 1, pp. 296–304 (1998)Google Scholar
  37. 37.
    Resnik, P.: Using information content to evaluate semantic similarity in a taxonomy (1995)Google Scholar
  38. 38.
    Wu, Z., Palmer, M.: Verbs semantics and lexical selection. In: Proceedings of the 32nd Annual Meeting on Association for Computational Linguistics, pp. 133–138 (1998)Google Scholar
  39. 39.
    Gruber, T.R.: A translation approach to portable ontologies. Knowledge Acquisition 5(2), 199–220 (1993)CrossRefGoogle Scholar
  40. 40.
    Zazzaro, G., Gigante, G., Zaccariello, E., Ficco, M., Di Martino, B.: Supporting Development of Certified Aeronautical Components by applying Text Analysis Technique. In: Proceedings of the International Conference on Complex, Intelligent and Software Intensive Systems, CISIS-2014 (July 2014)Google Scholar
  41. 41.
    Venticinque, A., Mazzocca, N., Venticinque, S., Ficco, M.: Semantic support for log analysis of Safety-Critical embedded systems. In: Proc. of the 13th European Dependable Computing Conference (EDCC 2014), Newcastle, UK (May 2014)Google Scholar
  42. 42.
    Ficco, M., Daidone, A., Coppolino, L., Romano, L., Bondavalli, A.: An event correlation approach for fault diagnosis in SCADA infrastructures. In: Proc. of the 13th European Workshop on Dependable Computing (EWDC 2011), pp. 15–20 (2011)Google Scholar
  43. 43.
    Leveson, N.: Completeness in formal specification language design for process-control systems. In: Proceedings of the Third Workshop on Formal Methods in Software Practice, pp. 75–87 (2000)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Gabriella Gigante
    • 1
    Email author
  • Francesco Gargiulo
    • 1
  • Massimo Ficco
    • 2
  1. 1.CIRA - Italian Aerospace Research CenterCapuaItaly
  2. 2.Department of Industrial and Information EngineeringSecond University of NaplesAversa (CE)Italy

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