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Global Software Development: A Design Framework to Measure the Risk of the Global Practitioners

Published:24 November 2017Publication History

ABSTRACT

Information Technology (IT) organizations are highly inspired to use the technological advancements in a rapid way to develop software products across different locations, irrespective of risk involved. Practitioners also readily grab the opportunities offered to showcase their talent, in spite of the difficulties they undergo. A global practitioner is exposed to risk in various phases of Global Software Development (GSD) due to the competitive environment he is in. Practitioner's risk affects the organization and is likely to result in project failure. Thus for successful completion of the project, it is essential to identify the risk in the various phases of GSD dynamically. This paper proposes a framework to dynamically assess the risk of the global practitioners using MultiAgent Simulation Model (MASM). Global practitioner agent and organizational agent are used in this work for risk assessment. This paper also explains a step-by-step risk assessment procedure. The risk level associated with each global practitioner is computed using two-valued binary logic and hence the risk associated with project completion is found using the probability equations.

References

  1. Agarwal, R., Umphress, D., 2010. A Flexible Model for Simulation of Software Development Process, ACM 48th Annual Southeast Regional Conference.Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Alshammri, M., 2015. Simulation Modelling of Human Aspects in Software Project Environment, ASWEC' 15, II, 145--146. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Baia, D., Lucena, C.J.P., Cowan, D., Bommel, P., Valadares, C., Oliveira, T., 2014. A MultiAgent-Based Simulation Model to Support Management Decision Making in Software Development, Technical Report CS-2014-04Google ScholarGoogle Scholar
  4. Baia, D. D., 2015. An Integrated Multi-Agent-Based Simulation Approach to Support Software Project Management, IEEE / ACM 37th IEEE International Conference on Software Engineering, Doctoral Symposium.Google ScholarGoogle ScholarCross RefCross Ref
  5. Baxter, G., Sommerville, I., 2008. Socio-technical systems: From design methods to systems engineering, Submitted to The journal of human-computer studies.Google ScholarGoogle Scholar
  6. Boissier, O., Bordini, R.H., Hubner, J. F., Ricci, A., Santi, A., 2013. Multi-agent oriented Programming with JaCaMo, Science of Computer Programming, 78, 6, 747--761. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Browning, T.R., 2014. A Quantitative Framework for Managing Project Value, Risk and Opportunity, IEEE Transactions on Engineering Management, 61, 4, 583--598. Google ScholarGoogle ScholarCross RefCross Ref
  8. Chamundeswari, A., Baskaran, K., 2016. Global Software development: An Approach to Design and Evaluate the Risk factors for Global Practitioners, 28th International Conference on Software Engineering & Knowledge Engineering, 565--568.Google ScholarGoogle Scholar
  9. Chamundeswari, A., Baskaran K., 2016. Global Software Development: A Design to Measure Risk of Global Practitioners, IEEE International conferences CAST-2016, College of Engineering Pune, India.Google ScholarGoogle Scholar
  10. Cherns, A.B., 1976. The principles of socio technical design, Human Relations, 29, 783--792. Google ScholarGoogle ScholarCross RefCross Ref
  11. Conchúir, E.O., Ågerfalk, P.J., Olsson, H.H., and Fitzgerald, B., 2009. Global software development: where are the benefits?, Communications of the ACM, 52, 127--131. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Eberlein, R., Diker, V., Langer, R., Rowe J., (Eds.), 2003. Proceedings of the 21st International Conference of the System Dynamics Society, 20--24.Google ScholarGoogle Scholar
  13. Ebert, C, Murthy, B.K., Jha, N.N., 2008. Managing Risks in Global Software Engineering: Principles and Practices, IEEE International Conference on Global Software Engineering, 131--140. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Joslin, D., Poole, W., 2005. Agent-Based Simulation For Software Project Planning, Proceedings of the 2005 Winter Simulation Conference. Google ScholarGoogle ScholarCross RefCross Ref
  15. Kellner, M. I, Madachy, R. J., Raffo, D. M., 1999. Software Process Modeling and Simulation: Why, What, How, Journal of Systems and Software, 46, 2/3.Google ScholarGoogle ScholarCross RefCross Ref
  16. Khezami, N., Otmane, S., Mallem, M., 2005. A New Formal Model of Collaboration by Multi-Agent Systems, Proc. IEEE KIMAS: 32--37, Massachusetts. Google ScholarGoogle ScholarCross RefCross Ref
  17. Lamersdorf A., Münch, J., Torre, A. F. V., Sánchez, C.R., 2011. A Risk-driven Model for Work Allocation in Global Software Development Projects, Sixth IEEE International Conference on Global Software Engineering, 15--24. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Lock, R., Sommerville, I, Socio Technical Systems Engineering Handbook.Google ScholarGoogle Scholar
  19. Lock, R., Sommerville, I., 2010. Modelling and Analysis of Socio-Technical System of Systems, 15th IEEE International Conference on Engineering of Complex Computer Systems, 224--232. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Luck M., McBurney, P., 2008. Computing as Interaction: Agent and Agreement Technologies.Google ScholarGoogle Scholar
  21. Moe, N.B., Smite, D., 2008. Understanding a Lack of Trust in Global Software Teams: A Multiple-case Study, Software Process Improvement and Practice, 13, 217--231. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Nurdiani, I., Jabangwe, R., Smite, D., Damian, D., 2011. Risk Identification and Risk Mitigation Instruments for Global Software Development: Systematic Review and Survey Results, Sixth IEEE International Conference on Global Software Engineering Workshops, 36--41. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. Pressman, R. 2005. Software Engineering: A Practitioner's Approach. McGraw-Hill.Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. Scholl, G. J. 1995. Benchmarking the system dynamics community: research results, System Dynamics Review 11,2, 139--155. Google ScholarGoogle ScholarCross RefCross Ref
  25. Šmite D., and Borzovs, J., 2008. Managing Uncertainty in Globally Distributed Software Development Projects, University of Latvia, Computer Science and Information Technologies, 733, 9--23.Google ScholarGoogle Scholar
  26. Usman, M., Azam, F., Hashmi, N., 2014. Analysing and reducing risk factor in 3-C's model communication phase used in global software development, International Conference on Information Science and Applications (ICISA), 1--4. Google ScholarGoogle ScholarCross RefCross Ref
  27. Verner, J. M., Brereton, O.P., Kitchenham, B.A., Turner, M., Niazi, M., 2014. Risks and risk mitigation in global software development: A tertiary study Information and Software Technology, 56, 54--78. Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. Wickenberg, T., Davidsson. P., 2002. On Multi Agent Based Simulation of Software Development Processes. In Multi-Agent-Based Simulation II, Edited by J. SimãoSichman, F. Bousquet, and P. Davidsson, 104--113.Google ScholarGoogle Scholar

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  • Published in

    cover image ACM Other conferences
    ICCCT-2017: Proceedings of the 7th International Conference on Computer and Communication Technology
    November 2017
    157 pages
    ISBN:9781450353243
    DOI:10.1145/3154979

    Copyright © 2017 ACM

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    • Published: 24 November 2017

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    Acceptance Rates

    ICCCT-2017 Paper Acceptance Rate33of124submissions,27%Overall Acceptance Rate33of124submissions,27%

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