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Estimating Factors of Agile Software Development Using Fuzzy Logic: A Survey

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Emergent Converging Technologies and Biomedical Systems (ETBS 2022)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 1040))

Abstract

Agile software development is an iterative process that concentrates on producing a minimal viable product (MVP) quickly and then adjusting and adding features and capabilities in phases based on user feedback and behaviour. It is the most popular software development model nowadays. However, there exists various factors that plays a vital role for software development and are unknown, vague, and imprecision during the software development. Fuzzy logic is widely used for estimation of these factors in agile software development. In this paper, a survey on the use of fuzzy logic in agile software development for estimating various factors is presented. A comparative table of various research articles on different parameters is presented. Various related research questions are framed and finally answered. Overall, it is intended that the study will help advance knowledge creation and information accumulation in the area of agile software development using fuzzy logic by offering readers and researchers a road map to direct future research.

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References

  1. Dybå T, Dingsøyr T (2005) About Agile software. IEEE Softw 26(5):6–9 (September–October 2009)

    Google Scholar 

  2. Islam AKZ, Ferworn DA (2020) A comparison between agile and traditional software development methodologies. Glob J Comput Sci Technol 7–42 (2020). https://doi.org/10.34257/gjcstcvol20is2pg7

  3. Leau YB, Loo WK, Tham WY, & Tan SF (2012) Software development life cycle AGILE vs traditional approaches. In: International Conference on Information and Network Technology, vol 37, no. 1, IACSIT Press, Singapore, pp 162–167

    Google Scholar 

  4. Cao L, Mohan K, Xu P, Ramesh B (2009) A framework for adapting agile development methodologies. Eur J Inf Syst 18(4):332–343. https://doi.org/10.1057/ejis.2009.26

    Article  Google Scholar 

  5. Mendel JM (1995) Fuzzy logic systems for engineering: a tutorial. Proc IEEE 83(3):345–377

    Google Scholar 

  6. Attarzadeh I, Ow SH (2009) Software development effort estimation based on a new fuzzy logic model. Int J Comput Theory Eng 473–476. https://doi.org/10.7763/ijcte.2009.v1.77

  7. Jeganathan C (2003, March) Development of fuzzy logic architecture to assess sustainability of the forest management, ITC

    Google Scholar 

  8. Jasem M, Laila R, Sulaiman L (2017) A fuzzy based model for effort estimation in scrum projects. Int J Adv Comput Sci Appl 8(9):270–277. https://doi.org/10.14569/ijacsa.2017.080939

  9. Bhatnagar R, Ghose MK, Bhattacharjee V (2011) Selection of defuzzification method for predicting the early stage software development effort using Mamdani FIS. Commun Comput Inf Sci 250 CCIS(4):375–381. https://doi.org/10.1007/978-3-642-25734-6_57

  10. Rola P, Kuchta D (2019) Application of fuzzy sets to the expert estimation of Scrum-based projects. Symmetry 11(8). https://doi.org/10.3390/sym11081032

  11. Kahraman C, Cebi S, Cevik Onar S, Oztaysi B, Tolga AC, Sari IU (eds) (2022) Intelligent and fuzzy techniques for emerging conditions and digital transformation, vol 307. https://doi.org/10.1007/978-3-030-85626-7

  12. Chrysafis KA, Papadopoulos BK (2021) Decision making for project appraisal in uncertain environments: a fuzzy-possibilistic approach of the expanded NPV method. Symmetry 13(1):1–24. https://doi.org/10.3390/sym13010027

    Article  Google Scholar 

  13. Assem H, Ramadan N (2016) A proposed fuzzy based framework for calculating success metrics of agile software projects. Int J Comput Appl 137(8):17–22. https://doi.org/10.5120/ijca2016908866

  14. Tayh A, Nagy RD, Hefny HA (2015) Towards a fuzzy based framework for effort estimation in agile software development. IJCSIS Int J Comput Sci Inf Secur 13(1):37–45. http://sites.google.com/site/ijcsis/

  15. Rai AK, Agarwal S, Kumar A (2018) A novel approach for agile software development methodology selection using fuzzy inference system. In: Proceedings of the international conference on smart systems and inventive technology, ICSSIT 2018, no. Icssit, pp 518–526. https://doi.org/10.1109/ICSSIT.2018.8748767

  16. Tran HQ (2020) Software development effort estimation using a fuzzy logic-based system within the context of the scaled agile framework. IOSR J Comput Eng 22(1):10–19. https://doi.org/10.9790/0661-2201021019

    Article  Google Scholar 

  17. Hamid M, Zeshan F, Ahmad A (2021) I. Conference, and undefined 2021. In: Fuzzy logic-based expert system for effort estimation in scrum projects. https://ieeexplore.ieee.org/abstract/document/9682239/. Accessed 23 Jul 2022

  18. Rai AK (2021) Agile software quality of design risk assessment using fuzzy logic international journal of engineering research & management technology agile software quality of design risk assessment using fuzzy logic. December 2021

    Google Scholar 

  19. Dursun M (2017) A fuzzy MCDM framework based on fuzzy measure and fuzzy integral for agile supplier evaluation. AIP Conf Proc 1836. https://doi.org/10.1063/1.4982006

  20. Dwivedi R, Gupta D (2017) The agile method engineering: applying fuzzy logic for evaluating and configuring agile methods in practice. Int. J. Comput. Aided Eng. Technol. 9(4):408–419. https://doi.org/10.1504/IJCAET.2017.086920

    Article  Google Scholar 

  21. Suresh M, Patri R (2017) Agility assessment using fuzzy logic approach: a case of healthcare dispensary. BMC Health Serv Res 17(1):1–13. https://doi.org/10.1186/s12913-017-2332-y

    Article  Google Scholar 

  22. Raslan AT, Darwish NR (2018) An enhanced framework for effort estimation of agile projects. Int J Intell Eng Syst 11(3):205–214. https://doi.org/10.22266/IJIES2018.0630.22

    Article  Google Scholar 

  23. Raslan AT, Darwish NR, Hefny HA (2015) Effort Estimation in agile software projects using fuzzy logic and story points. December 2015. http://www.researchgate.net/publication/288839279

  24. Dragicevic S, Celar S, Turic M (2017) Bayesian network model for task effort estimation in agile software development. J Syst Softw 127:109–119. https://doi.org/10.1016/j.jss.2017.01.027

    Article  Google Scholar 

  25. Ramadan N, Sabour AA, Darwish NR (2018) Adaptive fuzzy query approach for measuring time estimation and velocity in agile software development information security view project agile software development view project adaptive fuzzy query approach for measuring time estimation and velocity in Ag. Researchgate.Net, no. February 2020. https://www.researchgate.net/publication/326000239

  26. Saini A, Ahuja L, Khatri SK (2018) Effort estimation of agile development using fuzzy logic. In: 2018 7th international conference on reliability, Infocom technologies and optimization (trends and future directions) (ICRITO 2018), pp 779–783. https://doi.org/10.1109/ICRITO.2018.8748381

  27. Semenkovich SA, Kolekonova OI, Degtiarev KY (2017) A modified scrum story points estimation method based on fuzzy logic approach. In: Proceedings of the institute for system programming of RAS, vol 29, no 5, pp 19–38. https://doi.org/10.15514/ispras-2017-29(5)-2

  28. Sharma A, Bawa RK (2016) Modified fuzzy promethee approach for agile method selection using. I J C T a 9(41):641–649

    Google Scholar 

  29. Lin CT, Chiu H, Tseng YH (2006) Agility evaluation using fuzzy logic. Int J Prod Econ 101(2):353–368. https://doi.org/10.1016/j.ijpe.2005.01.011

    Article  Google Scholar 

  30. Masoumi M, Hossani S, Dehghani F, Masoumi A (2020) The challenges and advantages of fuzzy systems application. Researchgate, May, pp 01–07. https://doi.org/10.13140/RG.2.2.22310.96328

  31. Bansal S, Wadhawan S (2021) A hybrid of sine cosine and particle swarm optimization (HSPS) for solving heterogeneous fixed fleet vehicle routing problem. Int J Appl Metaheuristic Comput (IJAMC) 12(1):41–65

    Google Scholar 

  32. Bansal S, Goel R, Mohan C (2014) Use of ant colony system in solving vehicle routing problem with time window constraints. In: Proceedings of the Second International Conference on Soft Computing for Problem Solving (SocProS 2012). Springer, India, pp 39–50 December 28–30, 2012

    Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science and Engineering, MMEC, Maharishi Markandeshwar (Deemed to be University), Mullana, Ambala, Haryana, India

    Jahidul Hasan Antor, Sandhya Bansal &  Jamal

Authors
  1. Jahidul Hasan Antor
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  2. Sandhya Bansal
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  3. Jamal
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Corresponding author

Correspondence to Jahidul Hasan Antor .

Editor information

Editors and Affiliations

  1. Jaypee University of Information Technology, Solan, India

    Shruti Jain

  2. University Institute of Engineering and Technology, Kurukshetra University, Kurukshetra, Haryana, India

    Nikhil Marriwala

  3. NITTTR Bhopal, Bhopal, India

    C. C. Tripathi

  4. Department of Electrical and Computer System Engineering, RMIT University, Melbourne, VIC, Australia

    Dinesh Kumar

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Antor, J.H., Bansal, S., Jamal (2023). Estimating Factors of Agile Software Development Using Fuzzy Logic: A Survey. In: Jain, S., Marriwala, N., Tripathi, C.C., Kumar, D. (eds) Emergent Converging Technologies and Biomedical Systems. ETBS 2022. Lecture Notes in Electrical Engineering, vol 1040. Springer, Singapore. https://doi.org/10.1007/978-981-99-2271-0_19

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