Skip to main content
SpringerLink
Log in

Framework for prediction and classification of non functional requirements: a novel vision

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

Requirement Engineering has radicalized data analytics by playing a pivotal role in planning requirements strategies and activities. It has emerged as a leading domain of software engineering that branches into 2 categories namely functional requirements (FRs) and non-functional requirements (NFRs). Functional Requirements have gained eminence and prominence while NFRs have always faced a step-sisterly treatment. It is a lit large in the literature review that NFRs are very crucial and never considered appropriately and consequently, the systems have failed. The present study presents an automated system for efficient NFRs prediction. The proposed system consists of 4 layers for the prediction of NFRs significance including Data acquisition layer (DA), feature selection and extraction layer (FSE), data extraction and mining layer (DEM), and data analysis and visualization layer (DAV). Moreover, the current research considers the probability measure of the level of NFR significance in terms of LoNFRS, which is cumulatively quantified as NFRs significance measure (NFRINDEX). NFRINDEX has been quantified for prediction purposes using an convolution neural network (CNN). Additionally, the existence of NFRs is visualized based on a self organized mapping (SOM) procedure. To validate the proposed system, a primary dataset is collected from several IT professionals and academicians. The NFRs dataset of 312 IT professionals and academicians has been gathered for 17 attributes resulting in 5304 instances. Numerous experimental simulations were performed to assess performance in terms of classification, reliability, stability, feature selection, and prediction efficiency.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Notes

  1. Source:https://www.neuraldesigner.com/

References

  1. Jarzbowicz, A., Weichbroth, P.: A qualitative study on non-functional requirements in agile software development. IEEE Access 9, 40458–40475 (2021)

    Article  Google Scholar 

  2. Sandeepani, S., Nawinna, D.: Impact of non-functional requirements on the success of ubiquitous systems. In: 2020 2nd International Conference on Advancements in Computing (ICAC), volume 1, pages 263–268. IEEE, 2020

  3. Sommerville, I., Sawyer, P.: Requirements Engineering: A Good Practice Guide. Wiley, New York (1997)

    MATH  Google Scholar 

  4. Chung, L., Sampaio do Prado Leite, J.S.: On non-functional requirements in software engineering. In: Conceptual Modeling: Foundations and Applications, pp. 363–379. Springer (2009)

  5. Glinz, M.: Rethinking the notion of non-functional requirements. Proc. Third World Congress Softw. Qual. 2, 55–64 (2005)

    Google Scholar 

  6. Mairiza, D., Zowghi, D., Nurmuliani, N.: An investigation into the notion of non-functional requirements. In: Proceedings of the 2010 ACM Symposium on Applied Computing, pp. 311–317 (2010)

  7. Werner, C., Li, Z.S., Ernst, N., Damian, D.: The lack of shared understanding of non-functional requirements in continuous software engineering: Accidental or essential? In: 2020 IEEE 28th International Requirements Engineering Conference (RE), pp. 90–101. IEEE (2020)

  8. Handa, N., Sharma, A., Gupta, A.: An inclusive study of several machine learning based non-functional requirements prediction techniques. In: International Conference on Futuristic Trends in Networks and Computing Technologies, pp. 482–493. Springer (2019)

  9. Ameller, D., Franch, X., Gómez, C., Martínez-Fernández, S., Araújo, J., Biffl, S., Cabot, J., Cortellessa, V., Méndez, D., Moreira, A., et al.: Dealing with non-functional requirements in model-driven development: a survey. IEEE Trans. Softw. Eng. (2019)

  10. Kopczyńska, S., Ochodek, M., Nawrocki, J.: On importance of non-functional requirements in agile software projects-a survey. In: Integrating Research and Practice in Software Engineering, pp. 145–158. Springer (2020)

  11. Clancy, T.: The Standish Group Report. Chaos Report (1995)

  12. Ullah, S., Iqbal, M., Khan, A.M.: A survey on issues in non-functional requirements elicitation. In: International Conference on Computer Networks and Information Technology, pp. 333–340. IEEE (2011)

  13. Khan, F., Jan, S.R., Tahir, M., Khan, S., Ullah, F.: Survey: dealing non-functional requirements at architecture level. VFAST Trans. Softw. Eng. 9(2), 7–13 (2016)

    Article  Google Scholar 

  14. Eckhardt, J., Vogelsang, A., Fernández, D.M.: Are “non-functional” requirements really non-functional? an investigation of non-functional requirements in practice. In: Proceedings of the 38th International Conference on Software Engineering, pp. 832–842 (2016)

  15. Behutiye, W., Karhapää, P., Costal, D., Oivo, M., Franch, X.: Non-functional requirements documentation in agile software development: challenges and solution proposal. In: International Conference on Product-Focused Software Process Improvement, pp. 515–522. Springer (2017)

  16. Kopczyńska, S., Nawrocki, J.: Using non-functional requirements templates for elicitation: A case study. In: 2014 IEEE 4th International Workshop on Requirements Patterns (RePa), pp. 47–54. IEEE (2014)

  17. Davis, A.M., Leffingwell, D.A.: Using requirements management to speed delivery of higher quality applications. Ration. Softw. Corp. 20, 2004 (1996)

    Google Scholar 

  18. Cysneiros, L.M., Cesar Sampaio, J., Leite, P.: Nonfunctional requirements: from elicitation to conceptual models. IEEE Trans. Softw. Eng. 30(5), 328–350 (2004)

    Article  Google Scholar 

  19. Rahman, Md., Ripon, S., et al.: Elicitation and modeling non-functional requirements-a pos case study. arXiv preprint arXiv:1403.1936 (2014)

  20. Younas, M., Jawawi, D.N.A., Ghani, I., Kazmi, R.: Non-functional requirements elicitation guideline for agile methods. J. Telecommun. Electron. Comput. Eng. JTEC 9(3–4), 137–142 (2017)

    Google Scholar 

  21. Lindstrom, D.R.: Five ways to destroy a development project software development. IEEE Softw. 10(5), 55–58 (1993)

    Article  Google Scholar 

  22. Boehm, B., In, H.: Identifying quality-requirement conflicts. IEEE Softw. 13(2), 25–35 (1996)

    Article  Google Scholar 

  23. Breitman, K.K., Cesar S Leite, J., Finkelstein, A.: The world sa stage: a survey on requirements engineering using a real-life case study. J. Braz. Comput. Soc. 6(1), 13–37 (1999)

    Article  Google Scholar 

  24. Martens, N.: The impact of non-functional requirements on project success. Utrecht University, Msc Thesis, Utrecht (2011)

  25. Imran Babar, M., Ghazali, M., Jawawi, D.N.A.: Systematic reviews in requirements engineering: A systematic review. In: 2014 8th. Malaysian Software Engineering Conference (MySEC)

  26. Gordieiev, O., Kharchenko, V., Fominykh, N., Sklyar, V.: Evolution of software quality models in context of the standard iso 25010. In: Proceedings of the Ninth International Conference on Dependability and Complex Systems DepCoS-RELCOMEX. June 30–July 4, 2014, Brunów, Poland, pp. 223–232. Springer (2014)

  27. Ouhbi, S., Idri, A., Alemán, J.L.F., Toval, A., Benjelloun, H.: Applying iso/iec 25010 on mobile personal health records. In: HEALTHINF, pp. 405–412 (2015)

  28. Shiratuddin, N., et al.: Evaluation of e-book applications using iso 2010. In: 2015 International Symposium on Technology Management and Emerging Technologies (ISTMET) (2015)

  29. Haoues, M., Sellami, A., Ben-Abdallah, H., Cheikhi, L.: A guideline for software architecture selection based on iso 25010 quality related characteristics. Int. J. Syst. Assur. Eng. Manag. 8(2), 886–909 (2017)

    Article  Google Scholar 

  30. Bhatia, M., Sood, S.K.: A comprehensive health assessment framework to facilitate iot-assisted smart workouts: a predictive healthcare perspective. Comput. Ind. 92, 50–66 (2017)

    Article  Google Scholar 

  31. Bhatia, M., Sood, S.K., Kaur, S.: Quantum-based predictive fog scheduler for iot applications. Comput. Ind. 111, 51–67 (2019)

    Article  Google Scholar 

  32. Bhatia, M., Sood, S.K.: Exploring temporal analytics in fog-cloud architecture for smart office healthcare. Mob. Netw. Appl. 24(4), 1392–1410 (2019)

    Article  Google Scholar 

  33. Casamayor, A., Godoy, D., Campo, M.: Identification of non-functional requirements in textual specifications: a semi-supervised learning approach. Inf. Softw. Technol. 52(4), 436–445 (2010)

    Article  Google Scholar 

  34. Rahimi, M., Mirakhorli, M., Cleland-Huang, J.: Automated extraction and visualization of quality concerns from requirements specifications. In: 2014 IEEE 22nd international requirements engineering conference (RE), pp. 253–262. IEEE (2014)

  35. Bhatia, M., Kaur, S., Sood, S.K., Behal, V.: Internet of things-inspired healthcare system for urine-based diabetes prediction. Artif. Intell. Med. 107, 101913 (2020)

    Article  Google Scholar 

  36. Bhatia, M.: Fog computing-inspired smart home framework for predictive veterinary healthcare. Microprocess. Microsyst. 78, 103227 (2020)

    Article  Google Scholar 

  37. Bhatia, M., Sood, S.K.: Quantum computing-inspired network optimization for iot applications. IEEE Internet Things J. 7(6), 5590–5598 (2020)

    Article  Google Scholar 

  38. Ramadhani, D.A., Rochimah, S., Yuhana, U.L.: Classification of non-functional requirements using semantic-fsknn based iso/iec 9126. Telkomnika 13(4), 1456 (2015)

    Article  Google Scholar 

  39. Bhatia, M., Manocha, A.: Cognitive framework of food quality assessment in iot-inspired smart restaurants. IEEE Internet Things J. (2020)

  40. Bhatia, M., Ahanger, T.A.: Intelligent decision-making in smart food industry: quality perspective. Pervas. Mob. Comput. 72, 101304 (2021)

    Article  Google Scholar 

  41. Bhatia, M., Kaur, S., Sood, S.K.: Iot-inspired smart toilet system for home-based urine infection prediction. ACM Trans. Comput. Healthcare 1(3), 1–25 (2020)

    Article  Google Scholar 

  42. Bhatia, M.: Game theory based framework of smart food quality assessment. Trans. Emerg. Telecommun. Technol. 31(12), e3926 (2020)

    Google Scholar 

  43. Dias Canedo, E., Cordeiro Mendes, B.: Software requirements classification using machine learning algorithms. Entropy 22(9), 1057 (2020)

    Article  Google Scholar 

  44. Othman Ahmed, I.A., Elmubarak Daleel, M.E.: Automated use case diagram generation with non-functional requirements using neural network

  45. Naseem, R., Shaukat, Z., Irfan, M., Shah, M.A., Ahmad, A., Muhammad, F., Glowacz, A., Dunai, L., Antonino-Daviu, J., Sulaiman, A.: Empirical assessment of machine learning techniques for software requirements risk prediction. Electronics 10(2), 168 (2021)

    Article  Google Scholar 

  46. Kurtanović, Z., Maalej, W.: Automatically classifying functional and non-functional requirements using supervised machine learning. In: 2017 IEEE 25th International Requirements Engineering Conference (RE), pp. 490–495. Ieee (2017)

  47. Lu, M., Liang, P.: Automatic classification of non-functional requirements from augmented app user reviews. In: Proceedings of the 21st International Conference on Evaluation and Assessment in Software Engineering, pp. 344–353 (2017)

  48. Deocadez, R., Harrison, R., Rodriguez, D.: Automatically classifying requirements from app stores: A preliminary study. In: 2017 IEEE 25th International Requirements Engineering Conference Workshops (REW), pp. 367–371. IEEE (2017)

  49. Ezami, S.: Extracting non-functional requirements from unstructured text. Master’s thesis, University of Waterloo (2018)

  50. Di Martino, B., Pascarella, J., Nacchia, S., Maisto, S.A., Iannucci, P., Cerri, F.: Cloud services categories identification from requirements specifications. In: 2018 32nd International Conference on Advanced Information Networking and Applications Workshops (WAINA), pp. 436–441. IEEE (2018)

  51. Muhammad, Y., Shah, M.A., Jawawi, D.N.A., Ishfaq, M.K., Awais, M., Wakil, K., Mustafa, A.: Elicitation of nonfunctional requirements in agile development using cloud computing environment. IEEE Access (2020)

  52. Rahimi, N., Eassa, F., Elrefaei, L.: An ensemble machine learning technique for functional requirement classification. Symmetry 12(10), 1601 (2020)

    Article  Google Scholar 

  53. Haque, M.A., Rahman, M.A., Siddik, M.S.: Non-functional requirements classification with feature extraction and machine learning: An empirical study. In: 2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT), pp. 1–5. IEEE (2019)

  54. Kiran, H.M., Ali, Z.: Requirement elicitation techniques for open source systems: a review. Int. J. Adv. Comput. Sci. Appl. Pak., 330–334 (2018)

  55. Kopczyńska, S., Nawrocki, J., Ochodek, M.: An empirical study on catalog of non-functional requirement templates: usefulness and maintenance issues. Inf. Softw. Technol. 103, 75–91 (2018)

    Article  Google Scholar 

  56. Winkler, J.P., Vogelsang, A.: Using tools to assist identification of non-requirements in requirements specifications–a controlled experiment. In: International Working Conference on Requirements Engineering: Foundation for Software Quality, pp. 57–71. Springer (2018)

  57. Arruda, D.: Requirements engineering in the context of big data applications. ACM SIGSOFT Softw. Eng. Notes 43(1), 1–6 (2018)

    Article  Google Scholar 

  58. Kurtanovic, Z., Maalej, W.: Automatically classifying functional and non-functional requirements using supervised machine learning

  59. Rahman, M.A., Haque, M.A., Tawhid, M.N.A., Siddik, M.S.: Classifying non-functional requirements using rnn variants for quality software development. In: Proceedings of the 3rd ACM SIGSOFT International Workshop on Machine Learning Techniques for Software Quality Evaluation, pp. 25–30 (2019)

  60. Portugal, R.L., Li, T., Silva, L., Almentero, E., do Prado Leite, J.C.: Nfrfinder: a knowledge based strategy for mining non-functional requirements. In: Proceedings of the XXXII Brazilian Symposium on Software Engineering, pp. 102–111 (2018)

  61. Stanik, C., Haering, M., Maalej, W.: Classifying multilingual user feedback using traditional machine learning and deep learning. In: 2019 IEEE 27th International Requirements Engineering Conference Workshops (REW), pp. 220–226. IEEE (2019)

  62. Baker, C., Deng, L., Chakraborty, S., Dehlinger, J.: Automatic multi-class non-functional software requirements classification using neural networks. In: 2019 IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC), Vol. 2, pp. 610–615. IEEE (2019)

  63. Bhowmik, T., Quoc Do, A.: Refinement and resolution of just-in-time requirements in open source software and a closer look into non-functional requirements. J. Ind. Inf. Integr. 14, 24–33 (2019)

    Google Scholar 

  64. Ultsch, A.: Maps for the visualization of high-dimensional data spaces. In: Proc. Workshop on Self organizing Maps, pp. 225–230 (2003)

Download references

Acknowledgements

This research work has not been funded by any organization.

Author information

Authors and Affiliations

  1. Department of Computer Application, Lovely Professional University, Phagwara, India

    Naina Handa & Anil Sharma

  2. DAV College, Dasuya, India

    Amardeep Gupta

Authors
  1. Naina Handa
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Anil Sharma
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Amardeep Gupta
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Naina Handa.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Handa, N., Sharma, A. & Gupta, A. Framework for prediction and classification of non functional requirements: a novel vision. Cluster Comput 25, 1155–1173 (2022). https://doi.org/10.1007/s10586-021-03484-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-021-03484-0

Keywords

Search

Navigation