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Formal verification of extension of istar to support big data projects

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Języki publikacji
EN
Abstrakty
EN
Identifying all of the correct requirements of any system is fundamental for its success. These requirements need to be engineered with precision in the early phases. Principally, late correction costs are estimated to be more than 200 times greater than the cost of corrections during requirements engineering (RE), especially in the big data area due to its importance and characteristics. A deep analysis of the big data literature suggests that current RE methods do not support the elicitation of big data project requirements. In this research, we present BiStar (an extension of iStar) to undertake big data characteris tics such as volume, variety, etc. As a first step, some missing concepts are identified that are not supported by the current methods of RE. Next, BiStar is presented to take big data-specific characteristics into account while dealing with the requirements. To ensure the integrity property of BiStar, formal proofs are made by performing a Bigraph-based description on iStar and BiStar. Fi nally, iStar and BiStar are applied on the same exemplary scenario. BiStar shows promising results, so it is more efficient for eliciting big data project requirements.
Wydawca
Czasopismo
Rocznik
Tom
Strony
321–344
Opis fizyczny
Bibliogr. 38 poz., rys.
Twórcy
  • Mehri University, LIRE Laboratory, Algeria
  • Mehri University, LIRE Laboratory, Algeria
  • Jean Jaures University, IRIT Laboratory, France
Bibliografia
  • [1] Al-Najran N., Dahanayake A.: A Requirements Specification Framework for Big Data Collection and Capture. In: East European Conference on Advances in Databases and Information Systems, pp. 12–19, Springer, 2015.
  • [2] Ali R., Dalpiaz F., Giorgini P.: Requirements-driven deployment, Software & Systems Modeling, vol. 13(1), pp. 433–456, 2014.
  • [3] Anderson K.M.: Embrace the Challenges: Software Engineering in a Big Data World. In: 2015 IEEE/ACM 1st International Workshop on Big Data Software Engineering, pp. 19–25, IEEE, 2015.
  • [4] Arruda D.: Requirements Engineering in the Context of Big Data Applications, ACM SIGSOFT Software Engineering Notes, vol. 43(1), pp. 1–6, 2018.
  • [5] Arruda D., Madhavji N.H.: Towards a requirements engineering artefact model in the context of big data software development projects: Research in progress. In: IEEE International Conference on Big Data (Big Data), pp. 2314–2319, 2017.
  • [6] Arruda D., Madhavji N.H.: State of Requirements Engineering Research in the Context of Big Data Applications. In: International Working Conference on Requirements Engineering: Foundation for Software Quality, pp. 307–323, Springer, 2018.
  • [7] Attarha M., Modiri N.: Focusing on the importance and the role of require ment engineering. In: The 4th International Conference on Interaction Sciences, pp. 181–184, IEEE, 2011.
  • [8] Bersani M.M., Marconi F., Rossi M., Erascu M.: A tool for verification of big -data applications. In: Proceedings of the 2nd International Workshop on Quality-Aware DevOps, pp. 44–45, 2016.
  • [9] Chen H.M., Kazman R., Haziyev S., Hrytsay O.: Big Data System Development: An Embedded Case Study with a Global Outsourcing Firm. In: 2015 IEEE/ACM 1st International Workshop on Big Data Software Engineering, pp. 44–50, IEEE, 2015.
  • [10] Chen M., Mao S., Liu Y.: Big Data: A Survey, Mobile Networks and Applications, vol. 19(2), pp. 171–209, 2014.
  • [11] Clancy T.: The Standish Group Report CHAOS, Project Smart, pp. 1–16, 2014.
  • [12] Dalpiaz F., Franch X., Horkoff J.: iStar 2.0 Language Guide, arXiv preprint arXiv:160507767, 2016.
  • [13] Djeddi C., Zarour N.E., Charrel P.J.: Extension of iStar for Big Data Projects. In: International Conference on Advanced Aspects of Software Engineering, pp. 9–16, 2018.
  • [14] Eridaputra H., Hendradjaya B., Sunindyo W.D.: Modeling the requirements for big data application using goal oriented approach. In: 2014 International Conference on Data and Software Engineering (ICODSE), pp. 1–6, IEEE, 2014.
  • [15] Goncalves E., Castro J., Araujo J., Heineck T.: A Systematic Literature Review of iStar extensions, Journal of Systems and Software, vol. 137, pp. 1–33, 2018.
  • [16] Guzman A., Martınez-Rebollar A., Agudelo F.V., Estrada-Esquivel H., Or tega, J.P., Ortiz J.: A Methodology for Modeling Ambient Intelligence Appli cations using i* Framework. In: iStar, pp. 61–66, 2016.
  • [17] Jensen O.H., Milner R.: Bigraphs and mobile processes (revised). Technical re port, University of Cambridge, Computer Laboratory, 2004.
  • [18] Jutla D.N., Bodorik P., Ali S.: Engineering Privacy for Big Data Apps with the Unified Modeling Language. In: 2013 IEEE International Congress on Big Data, pp. 38–45, IEEE, 2013.
  • [19] Katal A., Wazid M., Goudar R.H.: Big data: Issues, challenges, tools and Good practices. In: 2013 Sixth International Conference on Contemporary Computing (IC3), pp. 404–409, IEEE, 2013.
  • [20] Keele S., et al.: Guidelines for performing Systematic Literature Reviews in Soft ware Engineering. Technical report, Ver. 2.3 EBSE Technical Report, 2007.
  • [21] Lamsweerde van A.: Goal-oriented requirements engineering: A guided tour. In: Proceedings Fifth IEEE International Symposium on Requirements Engineering, pp. 249–262, IEEE, 2001.
  • [22] Lau L.M.S., Yang-Turner F., Karacapilidis N.: Requirements for Big Data An alytics Supporting Decision Making: A Sensemaking Perspective. In: Mastering Data-Intensive Collaboration and Decision Making, pp. 49–70, Springer, 2014.
  • [23] Lockerbie J., Maiden N.A.M., Engmann J., Randall D., Jones S., Bush D.: Ex ploring the impact of software requirements on system-wide goals: a method using satisfaction arguments and i* goal modelling, Requirements Engineering, vol. 17(3), pp. 227–254, 2012.
  • [24] Madden S.: From Databases to Big Data, IEEE Internet Computing, vol. 16(3), pp. 4–6, 2012.
  • [25] Madhavji N.H., Miranskyy A., Kontogiannis K.: Big Picture of Big Data Soft ware Engineering: With Example Research Challenges. In: 2015 IEEE/ACM 1st International Workshop on Big Data Software Engineering, pp. 11–14, IEEE, 2015.
  • [26] Mazon J.N., Pardillo J., Trujillo J.: A Model-Driven Goal-Oriented Requirement Engineering Approach for Data Warehouses. In: International Conference on Conceptual Modeling, pp. 255–264, Springer, 2007.
  • [27] Morandini M., Penserini L., Perini A., Marchetto A.: Engineering requirements for adaptive systems, Requirements Engineering, vol. 22(1), pp. 77–103, 2017.
  • [28] Noorwali I., Arruda D., Madhavji N.H.: Understanding Quality Requirements in the Context of Big Data Systems. In: Proceedings of the 2nd International Workshop on BIG Data Software Engineering (BIGDSE), pp. 76–79, 2016.
  • [29] Nuseibeh B., Easterbrook S.: Requirements Engineering: A Roadmap. In: Proceedings of the Conference on the Future of Software Engineering, pp. 35–46, 2000.
  • [30] Otero C.E., Peter A.: Research Directions for Engineering Big Data Analytics Software, IEEE Intelligent Systems, vol. 30(1), pp. 13–19, 2014.
  • [31] Ramingwong L.: A review of requirements engineering processes, problems and models, International Journal of Engineering Science and Technology, vol. 4(6), pp. 2997–3002, 2012.
  • [32] Sachdeva V., Chung L.: Handling non-functional requirements for big data and IOT projects in Scrum. In: 2017 7th International Conference on Cloud Com puting, Data Science & Engineering-Confluence, pp. 216–221, IEEE, 2017.
  • [33] Sangeeta, Sharma K.: Quality issues with big data analytics. In: 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom), pp. 3589–3591, IEEE, 2016.
  • [34] Supakkul S., Zhao L., Chung L.: GOMA: Supporting Big Data Analytics with a Goal-Oriented Approach. In: 2016 IEEE International Congress on Big Data (BigData Congress), pp. 149–156, IEEE, 2016.
  • [35] Werneck V.M.B., de P´adua Albuquerque Oliveira A., do Prado Leite J.C.S.: Comparing GORE Frameworks: i-star and KAOS. In: Anais do WER09 – Work shop em Engenharia de Requisitos, Valpara´ıso, Chile, 2009.
  • [36] Yu E.: Modeling Strategic Relationships for Process Reengineering. In: E. Yu, P. Giorgini, N. Maiden, J. Mylopoulos (eds.), Social Modeling for Requirements Engineering, pp. 11–152, The MIT Press, 2011.
  • [37] Zave P.: Classification of research efforts in requirements engineering, ACM Computing Surveys (CSUR), vol. 29(4), pp. 315–321, 1997.
  • [38] Zowghi D., Coulin C.: Requirements Elicitation: A Survey of Techniques, Approaches, and Tools. In: Engineering and Managing Software Requirements, pp. 19–46, Springer, 2005.
Uwagi
PL
„Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).”
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-ad14c905-b28f-4ccc-8879-4558dea200f1
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