CFP last date
15 August 2024
Reseach Article

Mapping Software Requirements: An Overview of Classification Strategies

by Tamanna Yesmin Rashme
International Journal of Applied Information Systems
Foundation of Computer Science (FCS), NY, USA
Volume 12 - Number 45
Year of Publication: 2024
Authors: Tamanna Yesmin Rashme
10.5120/ijais2024451976

Tamanna Yesmin Rashme . Mapping Software Requirements: An Overview of Classification Strategies. International Journal of Applied Information Systems. 12, 45 ( Jul 2024), 1-6. DOI=10.5120/ijais2024451976

@article{ 10.5120/ijais2024451976,
author = { Tamanna Yesmin Rashme },
title = { Mapping Software Requirements: An Overview of Classification Strategies },
journal = { International Journal of Applied Information Systems },
issue_date = { Jul 2024 },
volume = { 12 },
number = { 45 },
month = { Jul },
year = { 2024 },
issn = { 2249-0868 },
pages = { 1-6 },
numpages = {9},
url = { https://www.ijais.org/archives/volume12/number45/mapping-software-requirements-an-overview-of-classification-strategies/ },
doi = { 10.5120/ijais2024451976 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-07-18T00:40:46.279638+05:30
%A Tamanna Yesmin Rashme
%T Mapping Software Requirements: An Overview of Classification Strategies
%J International Journal of Applied Information Systems
%@ 2249-0868
%V 12
%N 45
%P 1-6
%D 2024
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Effective software development relies on accurately identifying system requirements, which form the foundation for subsequent activities in the software life cycle (SLC). Proper classification of these requirements is crucial for subsequent design and implementation stages and can be achieved manually or through automated means. Machine Learning (ML) algorithms have emerged as valuable tools for addressing challenges in requirement engineering, such as identifying and prioritizing requirements. This paper examines ten studies to analyze the applied algorithms, presenting their training processes and evaluation metrics. The analysis aims to assist software engineers and researchers in selecting suitable requirement classification techniques by providing a detailed overview of various machine learning approaches and their effectiveness. Although the study references ten papers, it focuses on five key papers to demonstrate the results.

References
  1. Ashraf Abdulmunim Abdulmajeed, Younis S. Younis, “Supporting Classification of Software Requirements system Using Intelligent Technologies Algorithms,” Technium Vol. 3, Issue 11 pp.32-39 (2021) ISSN: 2668-778X.
  2. J. Manuel Perez-Verdejo, Angel J. S anchez-Garcıa, “A Systematic Literature Review on Machine Learning for Automated Requirements Classification”, 2020 8th International Conference in Software Engineering Research and Innovation (CONISOFT).
  3. Abualhaija, S., Arora, C., Sabetzadeh, M. et al. Automated demarcation of requirements in textual specifications: a machine learning-based approach. Empir Software Eng 25, 5454–5497 (2020). https://doi.org/10.1007/s10664-020-09864-1K.
  4. Du Zhang, Jeffrey, “Machine Learning and Software Engineering,” Software Quality Journal, vol. 11, no. 2, pp. 87–119, 2003. [Online]. Available: https://doi.org/10.1023/A:1023760326768.
  5. Sarker, I.H. Machine Learning: Algorithms, Real-World Applications and Research Directions. SN COMPUT. SCI. 2, 160 (2021). https://doi.org/10.1007/s42979-021-00592-x.
  6. P. Talele and R. Phalnikar, "Classification and Prioritisation of Software Requirements using Machine Learning – A Systematic Review," 2021 11th International Conference on Cloud Computing, Data Science & Engineering (Confluence), Noida, India, 2021, pp. 912-918, doi: 10.1109/Confluence51648.2021.9377190s
  7. Dias Canedo, E.; Cordeiro Mendes, B. Software Requirements Classification Using Machine Learning Algorithms. Entropy 2020, 22, 1057. https://doi.org/10.3390/e22091057.
  8. Law Foong Li, Nicholas Chia Jin-An, Zarinah Mohd Kasirun and Chua Yan Piaw, “An Empirical Comparison of Machine Learning Algorithms for Classification of Software Requirements” International Journal of Advanced Computer Science and Applications(IJACSA), 10(11), 2019. http://dx.doi.org/10.14569/IJACSA.2019.0101135.
  9. G. Y. Quba, H. Al Qaisi, A. Althunibat and S. AlZu’bi, "Software Requirements Classification using Machine Learning algorithm’s," 2021 International Conference on Information Technology (ICIT), Amman, Jordan, 2021, pp. 685-690, doi:10.1109/ICIT52682.2021.9491688
  10. Saad Shafiq, Atif, Christoph Mayr-Dorn, “Machine Learning for Software Engineering: A Systematic Mapping”, e-Informatica Software Engineering Journal (EISEJ), Volume 15, Issue 1, 2021, pages: 85–114, DOI 10.37190/e-Inf210105.
  11. Rajni Jindal, Ruchika Malhotra, Abha Jain and Ankita Bansal, "Mining Non-Functional Requirements using Machine Learning Techniques", In e-Informatica Software Engineering Journal, vol. 15, no. 1, pp. 85– 114, 2021. DOI: 10.37190/e-Inf210105.
  12. V. Patel, P. Mehta and K. Lavingia, "Software Requirement Classification Using Machine Learning Algorithms," 2023 International Conference on Artificial Intelligence and Applications (ICAIA) Alliance Technology Conference (ATCON-1), Bangalore, India, 2023, pp. 1-6, doi: 10.1109/ICAIA57370.2023.10169588.
  13. Pérez-Verdejo, J. & Sanchez Garcia, Angel & Ocharán-Hernández, Jorge. (2020). A Systematic Literature Review on Machine Learning for Automated Requirements Classification.21-28.10.1109/CONISOFT50191.2020.00014.
  14. Zhang, D., Tsai, J.J. Machine Learning and Software Engineering. Software Quality Journal 11, 87–119 (2003). https://doi.org/10.1023/A:1023760326768
  15. B. Kitchenham and S. Charters, “Guidelines for performing systematic literature reviews in software engineering,” Department of Computer Science University of Durham, Durham, UK, Tech. Rep., 2007.
  16. [Online]Avaible:http://promise.site.uottawa.ca/SERepository/datasets-page.html
  17. Rashme, T.Y., & Uddin, M.N. (2018). Self-Organizing Feature Map and K-Means Algorithm with Automatically Splitting and Merging Clusters based Image Segmentation. International Journal of Image, Graphics and Signal Processing, 10, 63-71.
  18. T. Y. Rashme, L. Islam, A. A. Prova and S. Jahan, "Autism Screening Disorder : Early Prediction," 2021 IEEE 4th International Conference on Computing, Power and Communication Technologies (GUCON), Kuala Lumpur, Malaysia, 2021, pp. 1-6, doi: 10.1109/GUCON50781.2021.9573547.
  19. T. Y. Rashme, L. Islam, S. Jahan and A. A. Prova, "Early Prediction of Cardiovascular Diseases Using Feature Selection and Machine Learning Techniques," 2021 6th International Conference on Communication and Electronics Systems (ICCES), Coimbatre, India, 2021, pp. 1554-1559, doi: 10.1109/ICCES51350.2021.9489057.
  20. “Machine Learning and Software Engineering,” Software QualityJournal, vol. 11, no. 2, pp. 87–119, 2003. [Online]. Available: https://doi.org/10.1023/A:1023760326768.
  21. Jahan, S., Islam, M.D.S., Islam, L. et al. Automated invasive cervical cancer disease detection at early stage through suitable machine learning model. SN Appl. Sci. 3, 806 (2021). https://doi.org/10.1007/s42452-021-04786-z.
  22. Hasan, T., Matin, A. (2021). Extract Sentiment from Customer Reviews: A Better Approach of TF-IDF and BOW-Based Text Classification Using N-Gram Technique. In: Uddin, M.S., Bansal, J.C. (eds) Proceedings of International Joint Conference on Advances in Computational Intelligence. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-16-0586-4_19.
  23. Ahammad, Tanvir & Ahamed, Md. Khabir & Reshmi, Tamanna & Karim, Abdul & Halder, Sajal & Hasan, Md Mahmudul. (2021). Identification of Abusive Behavior Towards Religious Beliefs and Practices on Social Media Platforms. International Journal of Advanced Computer Science and Applications. 12. 2021. 10.14569/IJACSA.2021.0120699.
  24. Sarker, I.H. Machine Learning: Algorithms, Real-World Applications and Research Directions. SN COMPUT. SCI. 2, 160 (2021). https://doi.org/10.1007/s42979-021-00592-x.
  25. Vijayvargiya, Sanidhya & Kumar, & Murthy, Lalita & Misra, Sanjay. (2022). Software Requirements Classification using Deep-learning Approach with Various Hidden Layers. 895-904. 10.15439/2022F140.
  26. Batta Mahesh, “Machine Learning Algorithms - A Review”, International Journal of Science and Research (IJSR) ISSN: 2319-7064 ResearchGate Impact Factor (2018): 0.28 | SJIF (2018): 7.426 Volume 9 Issue 1, January 2020
  27. [Online] Avaiable: https://pub.towardsai.net/difference-between-bagof-words-bow-and-tf-idf-in-nlp-with-python-97d3e75a9fd. Acces date: 5/5/2023.
  28. [Avaiable: https://www.analyticsvidhya.com/blog/2020/02/quickintroduction-bag-of-words-bow-tf-idf/, Acces date: 5/5/2023.
  29. John Violos,Konstantinos,“Text Classification Using the N-Gram Graph Representation Model Over High Frequency Data Streams,” Front. Appl. Math. Stat., 11 September 2018 Sec. Mathematics of Computation and Data Science Volume 4 - 2018 | https://doi.org/10.3389/fams.2018.00041.
  30. Pratvina Talele, Rasgmi Phalnikar,“Classification and Prioritisation of Software Requirements using Machine lEarning- A Systematic Review”, 2021 11th International Conference on Cloud Computing, Data Science & Engineering (Confluence) | 978-1-6654-1451-7/20/$31.00 ©2021 IEEE |DOI:10.1109/Confluence51648.2021.9377190.
Index Terms

Computer Science
Information Sciences

Keywords

SLC Software Requirement Classification Machine Learning.