Analyzing the Impact of Machine Learning Algorithms on Software Requirements Classification
DOI:
https://doi.org/10.21015/vtse.v13i1.2051Abstract
Along with the rapid growth of the world, the demand for efficient and successful software has increased swiftly. Any software has many steps for developing software and the most important step is software requirements engineering. Requirements classification can be applied manually, which requires great effort, time, and cost and the accuracy may vary. Many previous studies utilized machine learning algorithms to automate the classification process but traditional classification algorithms often require a large amount of labeled data, which can be expensive and time-consuming to collect. Few-Shot Learning (FSL) excels in situations with limited data, making it a promising alternative. This paper investigates the potential of applying Few-Shot Learning (FSL) algorithms for classifying software requirements. This study explores three prominent FSL algorithms: Prototypical Networks, Matching Networks, and Model-Agnostic Meta-Learning (MAML). These algorithms are evaluated on their ability to classify software requirements using a publicly available dataset. The results demonstrate that Prototypical Networks outperforms Matching Networks and MAML in this specific application. Matching Networks, designed for visual similarity tasks, struggle with textual data. Prototypical Networks achieve a remarkable accuracy of 82 percent, suggesting their effectiveness in learning class representations from a small number of samples. MAML also shows promising results with an accuracy of 76.9 percent. While acknowledging limitations in data pre-processing, the study concludes that FSL holds significant potential for efficient and cost-effective software requirement classification, particularly when dealing with limited labeled data.
References
K. Pohl, Requirements Engineering: Fundamentals, Principles, and Techniques. New York: Springer, 2011, pp. 53–55.
M. V. Mäntylä, F. Calefato, and M. Claes, “Natural language or not (NLON): A package for software engineering text analysis pipeline,” in Proc. 15th Int. Conf. Mining Softw. Repositories (MSR ’18), New York, NY, USA: ACM, 2018, pp. 387–391.
N. Qamar, N. Sabahat, A. Mashmool, and A. Mosavi, “Evaluating the impact of pair documentation on requirements quality and team productivity,” arXiv preprint arXiv:2304.14255, 2023.
D. Canedo and B. C. Mendes, “Software requirements classification using machine learning algorithms,” presented at the 2020 IEEE International Conference on Machine Learning, Sept. 2020.
H. Xu and G. Xu, “Software requirements classification with deep learning: A bibliometric review,” presented at the 2024 IEEE International Conference on Software Engineering, Mar. 2024.
Y. Wang, Q. Yao, J. T. Kwok, and L. M. Ni, “Generalizing from a few examples: A survey on few-shot learning,” unpublished, June 12, 2020.
Y. Liu et al., “Few-shot image classification: Current status and research trends,” Electronics, vol. 11, no. 11, p. 1752, 2022.
J. Winkler and A. Vogelsang, “Automatic classification of requirements based on convolutional neural networks,” in Proc. 2016 IEEE 24th Int. Requirements Eng. Conf. Workshops (REW), 2016, pp. 39–45.
G. Y. Quba, H. AlQaisi, A. Althunibat, and S. AlZu’bi, “Software requirements classification using machine learning algorithms,” in Proc. 2021 Int. Conf. Inf. Technol. (ICIT), 2021, pp. 685–690.
R. Jindal, R. Malhotra, and A. Jain, “Automated classification of security requirements,” in Proc. 2016 Int. Conf. Adv. Comput., Commun. Informatics (ICACCI), 2016, pp. 2027–2033.
Z. Kurtanovic and W. Maalej, “Automatically classifying functional and non-functional requirements using supervised machine learning,” in Proc. 2017 IEEE 25th Int. Requirements Eng. Conf. (RE), 2017, pp. 490–495.
S. Verma, “Analysis of strengths and weaknesses of SDLC models,” Int. J. Adv. Res. Comput. Sci. Manage. Stud., vol. 2, no. 3, 2014.
R. G. Sabale and A. R. Dani, “Comparative study of prototype model for software engineering with system development lifecycle,” IOSR J. Eng., vol. 2, no. 7, pp. 21–24, 2012.
R. Xiao, J. Wang, and F. Zhang, “An approach to incremental SVM learning algorithm,” in Proc. ISECS Int. Colloq. Comput., Commun., Control, Manage., 2008, vol. 1, pp. 352–354.
R. Navarro-Almanza, R. Juarez-Ramirez, and G. Licea, “Towards supporting software engineering using deep learning: A case of software requirements classification,” in Proc. 2017 5th Int. Conf. Softw. Eng. Res. Innov. (CONISOFT), 2017, pp. 116–120.
M. Lu and P. Liang, “Automatic classification of non-functional requirements from augmented app user reviews,” in Proc. 21st Int. Conf. Eval. Assess. Softw. Eng., Karlskrona, Sweden, 2017, pp. 344–353, doi: 10.1145/3084226.3084241.
G. Y. Quba, H. AlQaisi, A. Althunibat, and S. AlZu’bi, “Software requirements classification using machine learning algorithms,” in Proc. 2021 Int. Conf. Inf. Technol. (ICIT), 2021, pp. 685–690.
M. Sabir, C. Chrysoulas, and E. Banissi, “Multi-label classifier to deal with misclassification in non-functional requirements,” in Trends Innov. Inf. Syst. Technol., vol. 1, Springer, 2020, pp. 486–493.
M. K. Habib, S. Wagner, and D. Graziotin, “Detecting requirements smells with deep learning: Experiences, challenges and future work,” in Proc. 2021 IEEE 29th Int. Requirements Eng. Conf. Workshops (REW), 2021, pp. 153–156.
J. Snell, K. Swersky, and R. Zemel, “Prototypical networks for few-shot learning,” in Adv. Neural Inf. Process. Syst., vol. 30, 2017.
O. Vinyals, C. Blundell, T. Lillicrap, and D. Wierstra, “Matching networks for one-shot learning,” in Adv. Neural Inf. Process. Syst., vol. 29, 2016.
C. Finn, P. Abbeel, and S. Levine, “Model-agnostic meta-learning for fast adaptation of deep networks,” in Proc. Int. Conf. Mach. Learn., PMLR, 2017, pp. 1126–1135.
S. Kadam and V. Vaidya, “Review and analysis of zero, one and few-shot learning approaches,” in Proc. Int. Conf. Intell. Syst. Design Appl., 2018.
A. Melnikov et al., “A guide to few-shot learning with embeddings,” Medium, Aquarium Learning, Mar. 27, 2020.
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