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Modeling Regulatory Ambiguities for Requirements Analysis

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Conceptual Modeling (ER 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10650))

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Abstract

Lawyers and policy makers regularly and intentionally use ambiguous language in laws, regulations, and other legal texts. Although ambiguity has important policy benefits, such as interpretive resilience in an ever-changing world, it frustrates engineers and businesses seeking to build software systems that are demonstratively compliant with legal obligations. In this vision paper, we propose a method for modeling legal texts alongside models of software requirements or design artifacts. Our approach allows engineers to reason about regulatory ambiguity separately from their system under development and then trace interpretive decisions made about the legal text to affected requirements models. When a regulation is updated or case law demands a new interpretation of a regulation, engineers can evaluate the effect of the changes on the current design and respond appropriately. Inspired by User Requirements Notation, our proposed method can be implemented as an extension to Legal-GRL.

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Notes

  1. 1.

    http://eclipse.org/.

  2. 2.

    Pub. L. No. 104–191, 110 Stat. 1936 (1996).

  3. 3.

    All ambiguity identification is relative to the interpreter. There is no “ground truth” in ambiguity identification. However, for the sake of simplicity, we refer to Subpart (a)(1) as “containing” an ambiguity. In reality, without an interpreter, these same words are neither ambiguous nor unambiguous.

  4. 4.

    Again, based on our interpretation.

References

  1. Amyot, D.: JUCMNav. http://jucmnav.softwareengineering.ca/ucm/bin/view/ProjetSEG/WebHome, October (2016)

  2. Amyot, D., Ghanavati, S., Horkoff, J., Mussbacher, G., Peyton, L., Yu, E.: Evaluating goal models within the goal-oriented requirement language. Int. J. Intell. Syst. 25(8), 841–877 (2010)

    Article  Google Scholar 

  3. Amyot, D., Horkoff, J., Gross, D., Mussbacher, G.: A lightweight GRL profile for i* modeling. In: Heuser, C.A., Pernul, G. (eds.) ER 2009. LNCS, vol. 5833, pp. 254–264. Springer, Heidelberg (2009). doi:10.1007/978-3-642-04947-7_31

    Chapter  Google Scholar 

  4. Amyot, D., et al.: Towards advanced goal model analysis with jUCMNav. In: Castano, S., Vassiliadis, P., Lakshmanan, L.V., Lee, M.L. (eds.) ER 2012. LNCS, vol. 7518, pp. 201–210. Springer, Heidelberg (2012). doi:10.1007/978-3-642-33999-8_25

    Chapter  Google Scholar 

  5. Bhatia, J., Breaux, T.D., Reidenberg, J.R., Norton, T.B.: A theory of vagueness and privacy risk perception. In: 24th International RE Conference, Beijing, China, September 2016

    Google Scholar 

  6. Buhr, R., Casselman, R.: Use Case Maps for Object-Oriented Systems. Prentice-Hall, Upper Saddle River (1995)

    MATH  Google Scholar 

  7. Ghanavati, S.: Legal-URN Framework for Legal Compliance of Business Processes. PhD thesis, University of Ottawa, Ottawa, Canada (2013)

    Google Scholar 

  8. Gordon, D.G., Breaux, T.D.: Reconciling multi-jurisdictional legal requirements: a case study in requirements water marking. In: 20th IEEE International RE Conference, pp. 91–100, September 2012

    Google Scholar 

  9. ITU-T. User Requirements Notation (URN) – Language definition. Technical Report ITU-T Z.151, ITU-T, October 2012

    Google Scholar 

  10. Massey, A.K., Otto, P.N., Antón, A.I.: Evaluating legal implementation readiness decision-making. IEEE Trans. Softw. Eng. 41(6), 545–564 (2015)

    Article  Google Scholar 

  11. Massey, A.K., Otto, P.N., Hayward, L.J., Antón, A.I.: Evaluating existing security and privacy requirements for legal compliance. Requir. Eng. 15, 119–137 (2010)

    Article  Google Scholar 

  12. Massey, A.K., Rutledge, R.L., Antón, A.I., Hemmings, J.D., Swire, P.P.: A strategy for addressing ambiguity in regulatory requirements. https://smartech.gatech.edu/handle/1853/54573 (2015)

  13. Massey, A.K., Rutledge, R.L., Antón, A.I., Swire, P.P.: Identifying and classifying ambiguity for regulatory requirements. In: 22nd International Conference on RE, pp. 83–92, August 2014

    Google Scholar 

  14. Nigam, A., Arya, N., Nigam, B., Jain, D.: Tool for automatic discovery of ambiguity in requirements. Int. J. Comput. Sci. Issues 9(5) (2012)

    Google Scholar 

  15. Osborne, M., MacNish, C.K.: Processing natural language software requirement specifications. In: 2nd International Conference on RE, pp. 229–236, April 1996

    Google Scholar 

  16. Otto, P.N., Antón, A.I.: Addressing legal requirements in RE. In: 2007 15th IEEE International RE Conference, RE 2007, pp. 5–14 (2007)

    Google Scholar 

  17. Popescu, D., Rugaber, S., Medvidovic, N., Berry, D.M.: Reducing ambiguities in requirements specifications via automatically created object-oriented models. In: Paech, B., Martell, C. (eds.) Monterey Workshop 2007. LNCS, vol. 5320, pp. 103–124. Springer, Heidelberg (2008). doi:10.1007/978-3-540-89778-1_10

    Chapter  Google Scholar 

  18. Umber, A., Bajwa, I.S.: Minimizing ambiguity in natural language software requirements specification. In: 2011 Sixth International Conference on Digital Information Management, pp. 102–107, September 2011

    Google Scholar 

  19. van Bussel, D.: Detecting ambiguity in requirements specifications. PhD thesis, Tilburg University (2009)

    Google Scholar 

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Correspondence to Aaron K. Massey .

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Massey, A.K., Holtgrefe, E., Ghanavati, S. (2017). Modeling Regulatory Ambiguities for Requirements Analysis. In: Mayr, H., Guizzardi, G., Ma, H., Pastor, O. (eds) Conceptual Modeling. ER 2017. Lecture Notes in Computer Science(), vol 10650. Springer, Cham. https://doi.org/10.1007/978-3-319-69904-2_19

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  • DOI: https://doi.org/10.1007/978-3-319-69904-2_19

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