Research Article
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Year 2022, Volume: 2 Issue: 1, 1 - 6, 19.07.2022

Abstract

References

  • S. Schneider and A. Sunyaev, “Determinant factors of cloud-sourcing decisions: Reflecting on the IT outsourcing literature in the era of cloud computing,” Journal of Information Technology, vol. 31, no. 1, 2016, doi: 10.1057/jit.2014.25.
  • S. Dhar, “From outsourcing to Cloud computing: Evolution of IT services,” Management Research Review, vol. 35, no. 8, 2012, doi: 10.1108/01409171211247677.
  • S. Leimeister, M. Böhm, C. Riedl, and H. Krcmar, “The business perspective of cloud computing: Actors, roles, and value networks,” 2010.
  • M. Böhm, S. Leimeister, C. Riedl, and H. Krcmar, “Cloud Computing – Outsourcing 2.0 or a new Business Model for IT Provisioning?”, in Application Management, 2011. doi: 10.1007/978-3-8349-6492-2_2.
  • M. A. Akbar, M. Shameem, S. Mahmood, A. Alsanad, and A. Gumaei, “Prioritization based Taxonomy of Cloud-based Outsource Software Development Challenges: Fuzzy AHP analysis,” Applied Soft Computing Journal, vol. 95, no. 106557, 2020, doi: 10.1016/j.asoc.2020.106557.
  • S. U. Khan, M. Niazi, and R. Ahmad, “Factors influencing clients in the selection of offshore software outsourcing vendors: An exploratory study using a systematic literature review,” in Journal of Systems and Software, 2011, vol. 84, no. 4. doi: 10.1016/j.jss.2010.12.010.
  • A. B. Steven, Y. Dong, and T. Corsi, “Global sourcing and quality recalls: An empirical study of outsourcing-supplier concentration-product recalls linkages,” Journal of Operations Management, vol. 32, no. 5, 2014, doi: 10.1016/j.jom.2014.04.003.
  • R. Jabangwe, D. Šmite, and E. Hessbo, “Distributed software development in an offshore outsourcing project: A case study of source code evolution and quality,” Information and Software Technology, vol. 72, 2016, doi: 10.1016/j.infsof.2015.12.005.
  • D. Y. Chang, “Applications of the extent analysis method on fuzzy AHP,” European Journal of Operational Research, vol. 95, no. 3, pp. 649–655, 1996, doi: 10.1016/0377-2217(95)00300-2.
  • A. O. Kinay and B. T. Tezel, “Modification of the fuzzy analytic hierarchy process via different ranking methods,” International Journal of Intelligent Systems, pp. 1–29, 2021, doi: 10.1002/int.22628.
  • L. Zadeh, “Fuzzy Sets,” Information and Control, vol. 8, no. 3, pp. 338–353, 1965.
  • T. L. Saaty, The analytic hierarchy process: planning, priority setting, resource allocation, 1st edition. New York: McGraw-Hill International Book Co., 1980.
  • J. J. Buckley, “Fuzzy hierarchical analysis,” Fuzzy Sets and Systems, vol. 17, no. 3, pp. 233–247, 1985, doi: 10.1016/0165-0114(85)90090-9.
  • S. Abbasbandy and T. Hajjari, “A new approach for ranking of trapezoidal fuzzy numbers,” Computers and Mathematics with Applications, vol. 57, no. 3, pp. 413–419, 2009, doi: 10.1016/j.camwa.2008.10.090.
  • X. Wang and E. E. Kerre, “Reasonable properties for the ordering of fuzzy quantities (I),” Fuzzy Sets and Systems, vol. 118, no. 3, pp. 375–385, 2001, doi: 10.1016/S0165-0114(99)00062-7.
  • X. Wang and E. E. Kerre, “Reasonable properties for the ordering of fuzzy quantities (II),” Fuzzy Sets and Systems, vol. 118, no. 3, pp. 387–405, 2001, doi: 10.1016/S0165-0114(99)00063-9.
  • G. Bortolan and R. Degani, “A review of some methods for ranking fuzzy subsets,” Fuzzy Sets and Systems, vol. 15, no. 1, pp. 1–19, 1985, doi: 10.1016/0165-0114(85)90012-0.
  • N. van Hop, “Ranking fuzzy numbers based on relative positions and shape characteristics,” Expert Systems with Applications, vol. 191, 2022, doi: 10.1016/j.eswa.2021.116312.
  • Y. M. Wang and T. M. S. Elhag, “On the normalization of interval and fuzzy weights,” Fuzzy Sets and Systems, vol. 157, no. 18, pp. 2456–2471, 2006, doi: 10.1016/j.fss.2006.06.008.
  • M. G. Kendall, Rank Correlation Methods. 1948.
  • Y. M. Wang, Y. Luo, and Z. Hua, “On the extent analysis method for fuzzy AHP and its applications,” European Journal of Operational Research, vol. 186, no. 2, pp. 735–747, 2008, doi: 10.1016/j.ejor.2007.01.050.
  • K. Zhü, “Fuzzy analytic hierarchy process: Fallacy of the popular methods,” European Journal of Operational Research, vol. 236, no. 1, 2014, doi: 10.1016/j.ejor.2013.10.034.
  • S. Kubler, J. Robert, W. Derigent, A. Voisin, and Y. le Traon, “A state-of the-art survey & testbed of fuzzy AHP (FAHP) applications,” Expert Systems with Applications, vol. 65, pp. 398–422, 2016, doi: 10.1016/j.eswa.2016.08.064.
  • F. R. Lima-Junior and L. C. R. Carpinetti, “Dealing with the problem of null weights and scores in Fuzzy Analytic Hierarchy Process,” Soft Computing, vol. 24, no. 13, pp. 9557–9573, 2020, doi: 10.1007/s00500-019-04464-8.
  • F. Ahmed and K. Kilic, “Fuzzy Analytic Hierarchy Process: A performance analysis of various algorithms,” Fuzzy Sets and Systems, vol. 362, pp. 110–128, 2019, doi: 10.1016/j.fss.2018.08.009.

Providing Priority Degrees for the Challenges of Cloud-Based Outsource Software Development Projects via Fuzzy Analytic Hierarchy Process Methods

Year 2022, Volume: 2 Issue: 1, 1 - 6, 19.07.2022

Abstract

Cloud-based outsource software development (COSD) is a fairly new and popular software development methodology, which is enabled by the enormous growth of the cloud computing services in the last decade. The key idea of the methodology is to support software development processes of companies having software development team members from all around the world work collaboratively via cloud services. While there are quite some benefits a company could draw, there are also some challenges associated with the execution of a COSD project. It is intuitively essential to have a reliable way to assess a COSD project for its success. In this study, using Magnitude Based Fuzzy Analytic Hierarchy Process (MFAHP) as a method to prioritize and weight the challenges of a COSD project is presented. MFAHP is a fuzzy extension of the classical AHP which is shown to produce comparable results to other Fuzzy AHP (FAHP) methods with much smaller number of computations. The performance of the suggested methodology is evaluated and compared to Chang’s Fuzzy Extent Analysis on AHP (FEA) and Geometric Mean (GM) Methods, which are two other established FAHP methods. The results show that MFAHP and GM perform quite similar, whereas FEA gives inconsistent outputs. Among 21 different subcategories of COSD project challenges determined, “compatibility issues” are anticipated to have the highest weight individually while “organization management” is the most important of 4 main categories.

References

  • S. Schneider and A. Sunyaev, “Determinant factors of cloud-sourcing decisions: Reflecting on the IT outsourcing literature in the era of cloud computing,” Journal of Information Technology, vol. 31, no. 1, 2016, doi: 10.1057/jit.2014.25.
  • S. Dhar, “From outsourcing to Cloud computing: Evolution of IT services,” Management Research Review, vol. 35, no. 8, 2012, doi: 10.1108/01409171211247677.
  • S. Leimeister, M. Böhm, C. Riedl, and H. Krcmar, “The business perspective of cloud computing: Actors, roles, and value networks,” 2010.
  • M. Böhm, S. Leimeister, C. Riedl, and H. Krcmar, “Cloud Computing – Outsourcing 2.0 or a new Business Model for IT Provisioning?”, in Application Management, 2011. doi: 10.1007/978-3-8349-6492-2_2.
  • M. A. Akbar, M. Shameem, S. Mahmood, A. Alsanad, and A. Gumaei, “Prioritization based Taxonomy of Cloud-based Outsource Software Development Challenges: Fuzzy AHP analysis,” Applied Soft Computing Journal, vol. 95, no. 106557, 2020, doi: 10.1016/j.asoc.2020.106557.
  • S. U. Khan, M. Niazi, and R. Ahmad, “Factors influencing clients in the selection of offshore software outsourcing vendors: An exploratory study using a systematic literature review,” in Journal of Systems and Software, 2011, vol. 84, no. 4. doi: 10.1016/j.jss.2010.12.010.
  • A. B. Steven, Y. Dong, and T. Corsi, “Global sourcing and quality recalls: An empirical study of outsourcing-supplier concentration-product recalls linkages,” Journal of Operations Management, vol. 32, no. 5, 2014, doi: 10.1016/j.jom.2014.04.003.
  • R. Jabangwe, D. Šmite, and E. Hessbo, “Distributed software development in an offshore outsourcing project: A case study of source code evolution and quality,” Information and Software Technology, vol. 72, 2016, doi: 10.1016/j.infsof.2015.12.005.
  • D. Y. Chang, “Applications of the extent analysis method on fuzzy AHP,” European Journal of Operational Research, vol. 95, no. 3, pp. 649–655, 1996, doi: 10.1016/0377-2217(95)00300-2.
  • A. O. Kinay and B. T. Tezel, “Modification of the fuzzy analytic hierarchy process via different ranking methods,” International Journal of Intelligent Systems, pp. 1–29, 2021, doi: 10.1002/int.22628.
  • L. Zadeh, “Fuzzy Sets,” Information and Control, vol. 8, no. 3, pp. 338–353, 1965.
  • T. L. Saaty, The analytic hierarchy process: planning, priority setting, resource allocation, 1st edition. New York: McGraw-Hill International Book Co., 1980.
  • J. J. Buckley, “Fuzzy hierarchical analysis,” Fuzzy Sets and Systems, vol. 17, no. 3, pp. 233–247, 1985, doi: 10.1016/0165-0114(85)90090-9.
  • S. Abbasbandy and T. Hajjari, “A new approach for ranking of trapezoidal fuzzy numbers,” Computers and Mathematics with Applications, vol. 57, no. 3, pp. 413–419, 2009, doi: 10.1016/j.camwa.2008.10.090.
  • X. Wang and E. E. Kerre, “Reasonable properties for the ordering of fuzzy quantities (I),” Fuzzy Sets and Systems, vol. 118, no. 3, pp. 375–385, 2001, doi: 10.1016/S0165-0114(99)00062-7.
  • X. Wang and E. E. Kerre, “Reasonable properties for the ordering of fuzzy quantities (II),” Fuzzy Sets and Systems, vol. 118, no. 3, pp. 387–405, 2001, doi: 10.1016/S0165-0114(99)00063-9.
  • G. Bortolan and R. Degani, “A review of some methods for ranking fuzzy subsets,” Fuzzy Sets and Systems, vol. 15, no. 1, pp. 1–19, 1985, doi: 10.1016/0165-0114(85)90012-0.
  • N. van Hop, “Ranking fuzzy numbers based on relative positions and shape characteristics,” Expert Systems with Applications, vol. 191, 2022, doi: 10.1016/j.eswa.2021.116312.
  • Y. M. Wang and T. M. S. Elhag, “On the normalization of interval and fuzzy weights,” Fuzzy Sets and Systems, vol. 157, no. 18, pp. 2456–2471, 2006, doi: 10.1016/j.fss.2006.06.008.
  • M. G. Kendall, Rank Correlation Methods. 1948.
  • Y. M. Wang, Y. Luo, and Z. Hua, “On the extent analysis method for fuzzy AHP and its applications,” European Journal of Operational Research, vol. 186, no. 2, pp. 735–747, 2008, doi: 10.1016/j.ejor.2007.01.050.
  • K. Zhü, “Fuzzy analytic hierarchy process: Fallacy of the popular methods,” European Journal of Operational Research, vol. 236, no. 1, 2014, doi: 10.1016/j.ejor.2013.10.034.
  • S. Kubler, J. Robert, W. Derigent, A. Voisin, and Y. le Traon, “A state-of the-art survey & testbed of fuzzy AHP (FAHP) applications,” Expert Systems with Applications, vol. 65, pp. 398–422, 2016, doi: 10.1016/j.eswa.2016.08.064.
  • F. R. Lima-Junior and L. C. R. Carpinetti, “Dealing with the problem of null weights and scores in Fuzzy Analytic Hierarchy Process,” Soft Computing, vol. 24, no. 13, pp. 9557–9573, 2020, doi: 10.1007/s00500-019-04464-8.
  • F. Ahmed and K. Kilic, “Fuzzy Analytic Hierarchy Process: A performance analysis of various algorithms,” Fuzzy Sets and Systems, vol. 362, pp. 110–128, 2019, doi: 10.1016/j.fss.2018.08.009.
There are 25 citations in total.

Details

Primary Language English
Subjects Computer Software
Journal Section Research Articles
Authors

Ayşe Övgü Kınay 0000-0001-9908-8652

Can Atılgan 0000-0002-1680-6207

Publication Date July 19, 2022
Published in Issue Year 2022 Volume: 2 Issue: 1

Cite

APA Kınay, A. Ö., & Atılgan, C. (2022). Providing Priority Degrees for the Challenges of Cloud-Based Outsource Software Development Projects via Fuzzy Analytic Hierarchy Process Methods. Journal of Emerging Computer Technologies, 2(1), 1-6.
Journal of Emerging Computer Technologies
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Izmir Academy Association