• P-ISSN 0974-6846 E-ISSN 0974-5645

Indian Journal of Science and Technology

Article

PUGH Decision Trapezoidal Fuzzy and Gradient Reinforce Deep Learning for Large Scale Requirement Prioritization
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Indian Journal of Science and Technology

DOI: 10.17485/IJST/v15i12.1757

Year: 2022, Volume: 15, Issue: 12, Pages: 542-553

Original Article

PUGH Decision Trapezoidal Fuzzy and Gradient Reinforce Deep Learning for Large Scale Requirement Prioritization

Raghavendra Devadas1,2*, Nagaraj G Cholli3

1Research Scholar, RV College of Engineering, Visveswaraya Technological University, Belgaum
2Assistant Professor, Department of CSE, Presidency University, Bangalore
3Associate Professor. Department of ISE, RV College of Engineering, Bangalore

*Corresponding Author
Email: [email protected]

Received Date:21 September 2021, Accepted Date:18 February 2022, Published Date:25 March 2022

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Objective: To prioritize requirements for large scale software projects within time involving uncertainty in the opinions among different stakeholders. Methods: We propose Pugh Trapezoidal Fuzzy and Gradient Reinforce Learning (PTF-GRL) methods for large scale software requirement prioritization. A Pugh Decision-based Trapezoidal Fuzzy Requirement Selection model is designed, inputting the functional and non-functional requirements of the corresponding stakeholders. With the assistance of Trapezoidal Fuzzy Inference, the qualitative factors are mapped with the corresponding numeric factors, which increases the computational efficiency. Findings: Performance is analyzed based on four parameters: The first parameter is accuracy and our method showed improvement of 4%, 7% and 3% compared to JRD-SCRUM, IFS and SRPTackle respectively. The second parameter is prioritization time and found that our method had reduced time of 30%, 37% and 39% compared with existing methods. The third parameter is precision and it was found that our method improves precision by 6%, 10% and 5% compared with the other two methods. The final parameter we consider is the test suite execution and our method showed improvement of 12%, 19% and 5% compared with the existing two methods. Novelty/Applications: The originality of this work indicates the better performance along with the optimal test suite execution even considering the uncertainty factor in the proposed method compared with existing similar methods.

Keywords: Software Project; Pugh Decision Matrix; Trapezoidal Fuzzy Inference; Gradient Orientation; Reinforce Learning; Requirement Prioritization

References

  1. Saeeda H, Dong J, Wang Y, Abid MA. A proposed framework for improved software requirements elicitation process in SCRUM: Implementation by a real‐life Norway‐based IT project. Journal of Software: Evolution and Process. 2020;32(7). Available from: https://dx.doi.org/10.1002/smr.2247
  2. Gupta A, Gupta C. A novel collaborative requirement prioritization approach to handle priority vagueness and inter-relationships. Journal of King Saud University - Computer and Information Sciences. 2019. Available from: https://dx.doi.org/10.1016/j.jksuci.2019.12.002
  3. Hujainah F, Bakar RBA, Nasser AB, Al-haimi B, Zamli KZ. SRPTackle: A semi-automated requirements prioritisation technique for scalable requirements of software system projects. Information and Software Technology. 2021;131:106501. Available from: https://dx.doi.org/10.1016/j.infsof.2020.106501
  6. Seyff N, Todoran I, Caluser K, Singer L, Glinz M. Using popular social network sites to support requirements elicitation, prioritization and negotiation. Journal of Internet Services and Applications. 2015;6(1):1–16. Available from: https://dx.doi.org/10.1186/s13174-015-0021-9
  7. Ali S, Hafeez Y, Asghar S, Nawaz A, Saeed S. Aspect‐based requirements mining technique to improve prioritisation process: multi‐stakeholder perspective. IET Software. 2020;14(5):482–492. Available from: https://dx.doi.org/10.1049/iet-sen.2019.0332
  8. Zare F, Khademizare H. Software effort estimation based on the optimal Bayesian belief network. Applied Soft Computing. 2016;49:968–980. doi: 10.1016/j.asoc.2017.10.047
  9. Hernández-González J, Rodriguez D, Inza I, Harrison R, Lozano JA. Learning to classify software defects from crowds: A novel approach. Applied Soft Computing. 2018;62:579–591. Available from: https://dx.doi.org/10.1016/j.asoc.2017.10.047
  10. Abualhaija S, Arora C, Sabetzadeh M, Briand LC, Traynor M. Automated demarcation of requirements in textual specifications: a machine learning-based approach. Empirical Software Engineering. 2020;25(6):5454–5497. Available from: https://dx.doi.org/10.1007/s10664-020-09864-1
  11. Dingsøyr T, Moe NB, Fægri TE, Seim EA. Exploring software development at the very large-scale: a revelatory case study and research agenda for agile method adaptation. Empirical Software Engineering. 2018;23(1):490–520. Available from: https://dx.doi.org/10.1007/s10664-017-9524-2
  12. Shinkuma R, Nishio T. Data Assessment and Prioritization in Mobile Networks for Real-Time Prediction of Spatial Information with Machine Learning. 2019 IEEE First International Workshop on Network Meets Intelligent Computations (NMIC). 2019;92:1–19. doi: 10.1016/j.asoc.2016.07.040
  13. Xue Y, Zhong J, Tan TH, Liu Y, Cai W, Chen M, et al. IBED: Combining IBEA and DE for optimal feature selection in software product line engineering. Applied Soft Computing. 2016;49:1215–1231. Available from: https://dx.doi.org/10.1016/j.asoc.2016.07.040
  16. Heikkilä VT, Paasivaara M, Lasssenius C, Damian D, Engblom C. Managing the requirements flow from strategy to release in large-scale agile development: a case study at Ericsson. Empirical Software Engineering. 2017;22(6):2892–2936. Available from: https://dx.doi.org/10.1007/s10664-016-9491-z
  18. Daneva M, Veen Evd, Amrit C, Ghaisas S, Sikkel K, Kumar R, et al. Agile requirements prioritization in large-scale outsourced system projects: An empirical study. Journal of Systems and Software. 2013;86(5):1333–1353. Available from: https://dx.doi.org/10.1016/j.jss.2012.12.046
  19. Barbosa PAM, Pinheiro PR, Silveira FRV, Filho MS. Selection and Prioritization of Software Requirements Applying Verbal Decision Analysis. Complexity. 2019;2019:1–20. Available from: https://dx.doi.org/10.1155/2019/2306213
  20. Dabbagh M, Lee SP. An Approach for Integrating the Prioritization of Functional and Nonfunctional Requirements. The Scientific World Journal. 2014;2014:1–13. Available from: https://dx.doi.org/10.1155/2014/737626
  21. DJ, Michael I, Jasser M, , . Software Requirements Prioritization Tool using a Hybrid Technique. International Journal of Engineering and Advanced Technology. 2019;9(1):1631–1635. Available from: https://dx.doi.org/10.35940/ijeat.a2634.109119
  22. Sarker KU. Explicit specification framework to manage software project effectively. Indian Journal of Science and Technology. 2020;13(36):3785–3800. doi: 17485/IJST/v13i36.1244
  23. Tompkins M, Iammartino R, Fossaceca J. Multiattribute Framework for Requirements Elicitation in Phased Array Radar Systems. IEEE Transactions on Engineering Management. 2020;67(2):347–364. Available from: https://dx.doi.org/10.1109/tem.2018.2878688
  24. Zhou ZQ, Liu C, Chen TY, Tse TH, Susilo W. Beating Random Test Case Prioritization. IEEE Transactions on Reliability. 2021;70(2):654–675. Available from: https://dx.doi.org/10.1109/tr.2020.2979815
  25. Bajaj A, Sangwan OP. Test Case Prioritization Using Bat Algorithm. Recent Advances in Computer Science and Communications. 2021;14(2):593–598. Available from: https://dx.doi.org/10.2174/2213275912666190226154344
  26. Mughal S, Abbas A, Ahmad N, Khan SU. A Social Network Based Process to Minimize In-Group Biasedness During Requirement Engineering. IEEE Access. 2018;6:66870–66885. Available from: https://dx.doi.org/10.1109/access.2018.2879385
  27. Kamal T, Zhang Q, Akbar MA, Shafiq M, Gumaei A, Alsanad A. Identification and Prioritization of Agile Requirements Change Management Success Factors in the Domain of Global Software Development. IEEE Access. 2020;8:44714–44726. Available from: https://dx.doi.org/10.1109/access.2020.2976723
  28. AbdElazim K, Moawad R, Elfakharany E. A Framework for Requirements Prioritization Process in Agile Software Development. Journal of Physics: Conference Series. 2020;1454(1):012001. Available from: https://dx.doi.org/10.1088/1742-6596/1454/1/012001
  29. Khan AA, Shameem M, Kumar RR, Hussain S, Yan X. Fuzzy AHP based prioritization and taxonomy of software process improvement success factors in global software development. Applied Soft Computing. 2019;83:105648. Available from: https://dx.doi.org/10.1016/j.asoc.2019.105648
  30. Shameem M, Kumar RR, Kumar C, Chandra B, Khan AA. Prioritizing challenges of agile process in distributed software development environment using analytic hierarchy process. Journal of Software: Evolution and Process. 2018;30(11):e1979. Available from: https://dx.doi.org/10.1002/smr.1979
  32. Khan AA, Shameem M, Nadeem M, Akbar MA. Agile trends in Chinese global software development industry: Fuzzy AHP based conceptual mapping. Applied Soft Computing. 2021;102(3):107090. Available from: https://dx.doi.org/10.1016/j.asoc.2021.107090
  33. Devadas R, Cholli N. Interdependency aware QUBIT and BROWNBOOST rank for large scale requirement prioritization. International Journal of Computing and Digital Systems. 2022;11:625–634. Available from: http://dx.doi.org/10.12785/ijcds/110150

Copyright

© 2022 Devadas & Cholli. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Published By Indian Society for Education and Environment (iSee)

  • 26 March 2022

Year: 2022, Volume: 15, Issue: 12

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