Selection of software requirements using TOPSIS under fuzzy environment

References

• Pressman R. Software engineering – a practitioner’s approach. 7th ed. New York (NY): McGraw-Hill; 2010.
   Google Scholar
• Hughes DL, Dwivedi YK, Rana NP, et al. Information systems project failure – analysis of causal links using interpretive structural modelling. Product Plan Control Manag Oper. 2016;27(16):1313–1333.
   Web of Science ®Google Scholar
• Sadiq M. A fuzzy-set based approach for the prioritization of stakeholders on the basis of the importance of software requirements. IEEE J Res. 2017;63(5):616–629.
   Google Scholar
• Sadiq M, Jain S K. Applying fuzzy preference relation for requirements prioritization in goal oriented requirements elicitation process. Int J Syst Assur Eng Manage. 2014;5(4):711–723.
   Google Scholar
• Sadiq M, Jain SK. A fuzzy based approach for the selection of goals in goal oriented requirements elicitation process. Int J Syst Assur Eng Manage. 2015;6(2):157–164.
   Web of Science ®Google Scholar
• Koksalmis E, Kabak O. Deriving decision makers’ weight in group decision making: an overview of objective methods. Inf Fusion. 2019;49:146–160.
   Web of Science ®Google Scholar
• Wang XT, Xiong W. An integrated linguistic-based group decision-making approach for quality function deployment. Experts Syst Appl. 2011;38(12):14428–14438.
   Web of Science ®Google Scholar
• Zadeh LA. Fuzzy logic = computing with words. IEEE Trans Fuzzy Syst. 1996;4(2):103–111.
   Web of Science ®Google Scholar
• Shidpour H, Cunha CD, Bernard A. Group multi-criteria design concept evaluation using combined rough set and fuzzy set theory. Expert Syst Appl. 2016;64:633–644.
   Web of Science ®Google Scholar
• Mohammad CW, Khan S, Sadiq M. Software requirements selection using different patterns of pairwise comparison matrices of analytic hierarchy process. In 2nd International Conference on Communication and Computing Systems, Proceeding published by CRC Press; 2018 Dec, Organized by Dronacharya College of Engineering, Gurgaon, Haryana. Taylor and Francis.
   Google Scholar
• Behzadian M, Otaghsara SK, Yazdani M, et al. A state-of the-art survey of TOPSIS applications. Expert Syst Appl. 2012;39:13051–13069.
   Web of Science ®Google Scholar
• Mardani A, Jusoh A, Nor KMD, et al. Multiple criteria decision making techniques and their applications – a review of the literature from 2000 t0 2014. Econ Res. 2015;28(1):516–571.
   Google Scholar
• Karlsson J, Wohlin C, Regnell B. An evaluation of methods for prioritizing software requirements. Inf Softw Technol. 1998;39:939–947.
   Web of Science ®Google Scholar
• Perini A, Ricca F, Susi A. Tool-supported requirements prioritization: comparing the AHP and CBRank methods. Inf Softw Technol. 2009;51(6):1021–1032.
   Web of Science ®Google Scholar
• Siddiqui S, Beg MR, Fatima S. Effectiveness of requirement prioritization using analytical hierarchy process (AHP) and planning game (PG): a comparative study. Int J Comp Sci Inf Technol. 2013;4(1):46–49.
   Google Scholar
• Dabbagh M, Lee SP, Parizi RM. Functional and non-functional requirements prioritization: empirical evaluation of IPA, AHP-based, and HAM-based approaches. Soft comput. 2016;20(11):4497–4520.
   Web of Science ®Google Scholar
• Karlsson J, Ryan K. Supporting the selection of software requirements. In 8th IEEE International Workshop on Software Specification and Design; 1996. p. 146–149.
   Google Scholar
• Liu CH, Ting CK. Computational intelligence in music composition. IEEE Trans Emerg Top Comput Intell. 2017;1(1):2–15.
   Google Scholar
• Bian Y, Li Z, Zhao R, et al. Epistasis based ACO for regression test case prioritization. IEEE Trans Emerg Top Comput Intell. 2017;1(3):213–223.
   Google Scholar
• Wang T, Zhu J, Chen X, et al. A three layer privacy preserving cloud storage scheme based on computational intelligence in fog computing. IEEE Trans Emerg Top Comput Intell. 2018;2(1):3–12.
   Google Scholar
• Osman OF, Yap M. Computational intelligence in automatic face age estimation: a survey. IEEE Trans Emerg Top Comput Intell. 2019;3(3):271–285.
   Google Scholar
• Sadiq M, Afrin A. An integrated approach for the selection of software requirements using fuzzy AHP and Fuzzy TOPSIS method. In IEEE International Conference on Intelligent Computing, Instrumentation, and Control Technologies. Kannur; 2017. p. 1094–1100.
   Google Scholar
• Belenson SM, Kapur KC. An algorithm for solving multicriterion linear programming problems with examples. Oper Res Quart. 1973;24(1):65–77.
   Google Scholar
• Zeleny M. A concept of compromise solutions and the method of the displaced ideal. Comp Oper Res. 1974;1:479–496.
   Google Scholar
• Hwang CL, Yoon K. Multiple attributes decision making methods and application. Springer; 1981.
   Google Scholar
• Kim G, Park CS, Yoon KP. Identifying investment opportunities for advanced manufacturing system with competitive-integrated performance. Int J Prod Econ. 1997;50:23–33.
   Web of Science ®Google Scholar
• Shih H-S, Shyur H-J, Lee ES. An extension of TOPSIS for group decision making. Math Comput Model. 2007;45:801–813.
   Web of Science ®Google Scholar
• Zanakis SH, Solomon A, Wishart N, et al. Multi-attribute decision making: a simulation comparison of selection methods. Eur J Oper Res. 1998;107:507–529.
   Web of Science ®Google Scholar
• Junior FRL, Osiro L, Carpinetti LCR. A comparison between fuzzy AHP and fuzzy TOPSIS methods to supplier selection. Appl Soft Comput. 2014;21:194–209.
   Web of Science ®Google Scholar
• Ertugrul I, Karakasoglu N. Comparison of fuzzy AHP and fuzzy TOPSIS methods for facility location selection. Int J Adv Manuf Technol. 2008;39:783–795.
   Web of Science ®Google Scholar
• Cheng DY, Chao KM, Lo CC, et al. A user centric service-oriented modelling approach. World Wide Web. 2011;14:431–459.
   Google Scholar
• Zadeh LA. Fuzzy sets as a basis for a theory of possibility. Fuzzy Set Syst. 1978;1:3–28.
   Google Scholar
• Klir GJ, Yuan B. Fuzzy sets and fuzzy logic – theory and applications. Delhi: PHI Learning Private Limited; 2013.
   Google Scholar
• Biswas B, Sen BK. Medical image fusion using type-2 fuzzy and near-fuzzy set approach. Int J Comput Appl. 2020;42(4):399–414.
   Google Scholar
• Ya W, Meng-Ran Z, Lei N, et al. Trust analysis of WSN nodes based on fuzzy theory. Int J Comput Appl. 2020;42(1):52–56.
   Google Scholar
• Chen CT. Extension of the TOPSIS for group decision making under fuzzy environment. Fuzzy Sets Syst. 2000;114:1–9.
   Web of Science ®Google Scholar
• Chung L, Leite JCSP. On Non-functional requirements in software engineering. Concept Model Found Appl (LNCS). 2009;5600:363–379.
   Google Scholar
• Sadiq M, Jain SK. An insight into requirements engineering processes. In 3rd International Conference on Advances in Communication, Network, and Computing, LNCSIT. Chennai: Springer; 2012. p. 313–318.
   Google Scholar
• Sadiq M, Jain SK. A fuzzy based approach for requirements prioritization in goal oriented requirements elicitation process. In 25th International Conference on Software Engineering and Knowledge Engineering, Boston, MA; 2013 Jun 27–29.
   Google Scholar
• Khan S, Mohammad CW, Sadiq M. Generating patterns and sub-patterns of pairwise comparison matrices for the selection of software requirements. In IEEE International Conference on Advances in Computing, Communication, Control, and Networking, Organized by the Galgotias College of Engineering and Technology, Greater Noida; Oct 2018.
   Google Scholar
• Chen CT, Lin CT, Huang SF. A fuzzy approach for supplier evaluation and selection in supply chain management. Int J Prod Econ. 2006;102:289–301.
   Web of Science ®Google Scholar
• https://docs.google.com/forms/d/e/1FAIpQLSe-cx3IMb3VuB9gsy-I_nNeL-qn58urx TpJx A679wWMvK_VnA/viewform?usp=sf_link.
   Google Scholar
• Anvari A, Pan L, Zheng X. Generating security questions for better protection of user privacy. Int J Comput Appl. 2020;42(4):329–350.
   Google Scholar
• Lim SL, Finkelstein A. Stakerare: using social network and collaborative filtering for large-scale requirements elicitation. IEEE Trans Softw Eng. 2012;38(3):707–735.
   Web of Science ®Google Scholar
• Kahraman C, Ates NY, Cevik S, et al. Fuzzy multi-attribute cost-benefit analysis of e-services. Int J Intell Syst. 2007;22:547–565.
   Web of Science ®Google Scholar
• Kahraman C, Engin O, Kabak O, et al. Information systems outsourcing decisions using a group decision-making approach. Eng Appl Artif Intell. 2009;22(6):832–841.
   Web of Science ®Google Scholar
• Onut S, Kara S, Isik E. Long Term supplier selection using a Combined fuzzy MCDM approach: a case study for a telecommunication company. Expert Syst Appl. 2009;36:3887–3895.
   Web of Science ®Google Scholar
• Sun CC, Lin GTR. Using fuzzy TOPSIS method for evaluating the competitive advantages of shopping websites. Expert Syst Appl. 2009;36:11764–11771.
   Web of Science ®Google Scholar
• Kelemenis A, Askounis D. A new TOPSIS- based multi-criteria approach to personnel selection. Expert Syst Appl. 2010;37:4999–5008.
   Web of Science ®Google Scholar
• Kelemenis A, Ergazakis K, Askounis D. Support Managers selection using an extension of fuzzy TOPSIS. Expert Syst Appl. 2011;38:27774–22782.
   Web of Science ®Google Scholar
• Karlsson J, Ryan K. A cost-value approach for prioritizing requirements. IEEE Softw. 1997;14(5):67–74.
   Web of Science ®Google Scholar
• Karlsson J, Olsson S, Ryan K. Improved practical support for large-scale requirements prioritising. Req Eng. 1997;2(1):51–60.
   Google Scholar
• Sadiq M, Afrin A. Extending AHP_GORE_PSR by generating different patterns of pairwise comparison matrix. In International Conference on Information, Communication and Computing Technology, International Conference on Information, Communication and Computing Technology, Part of the Communications in Computer and Information Science book series (CCIS, vol. 750). New Delhi; 2017, p. 233–245.
   Google Scholar
• Ahmad KS, Ahmad N N, Tahir H, et al. Fuzzy_MoSCoW: a fuzzy based MoSCoW method for the prioritization of software requirements. In IEEE International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT), Kannur, India; 2017 Jul 06–07.
   Google Scholar
• Sadiq M, Khan S, Mohammad CW. Software requirements selection using Consistent pairwise comparison Matrices of AHP. Int J Comp Sci Eng. 2019;6(9):168–175
   Google Scholar