Abstract

Machine learning (ML) algorithms have proven effective in automating processes across various domains, including software engineering. One of the earliest and most critical phases of software development is requirements engineering (RE), which often involves manual tasks prone to human error and inefficiency. This research aims to enhance and automate the requirements engineering process by integrating advanced machine learning techniques, particularly in the areas of natural language processing (NLP) and pre-trained models such as BERT. By leveraging these technologies, we seek to reduce development costs, improve accuracy, and streamline the transition from requirements gathering to system design. The study involves the application and evaluation of a range of machine learning algorithms to determine the most suitable approaches for automating RE tasks. Finally, we propose a set of evaluation metrics to assess the effectiveness and practicality of the developed methods in real-world software requirement specification scenarios.

References

• V. Rooijen, B¨aumer, Platenius, Geierhos, Hamann, and Engels, From user demand to software service: Using machine learning to automate the requirements specification process, Jan. 1970. [Online]. Available: https:// ris.uni - paderborn.de/publication/97.
• K. Ryan, “The role of natural language in requirements engineering,” in
• Proceedings of the IEEE International Symposium on Requirements Engineering, IEEE, 1993, pp. 240–242.
• H. Zhu and L. Jin, “Automating scenario-driven structured requirements engineering,” in Proceedings 24th Annual International Computer Software and Applications Conference. COMPSAC2000, IEEE, 2000, pp. 311–316.
• A. Ning, H. Hou, Q. Hua, B. Yu, and K. Hao, “Requirements engineering processes improvement: A systematic view,” in Software Process Workshop, Springer, 2005, pp. 151–163.
• L. Jiang, A. Eberlein, B. H. Far, and M. Mousavi, “A methodology for the selection of requirements engineering techniques,” Software & Systems Modeling, vol. 7, no. 3, pp. 303–328, 2008.
• Z. J. Oster, G. R. Santhanam, and S. Basu, “Automating analysis of qualitative preferences in goal-oriented requirements engineering,” in 2011 26th IEEE/ACM International Conference on Automated Software Engineering (ASE 2011), IEEE, 2011, pp. 448–451.
• A. Chakraborty, M. K. Baowaly, A. Arefin, and A. N. Bahar, “The role of requirement engineering in software development life cycle,” Journal of emerging trends in computing and information sciences, vol. 3, no. 5, pp. 723–729, 2012.
• J. H. Hayes, W. Li, and M. Rahimi, “Weka meets tracelab: Toward convenient classification: Machine learning for requirements engineering problems: A position paper,” in 2014 IEEE 1st International Workshop on Artificial Intelligence for Requirements Engineering (AIRE), IEEE, 2014, pp. 9–12.
• E. Parra, C. Dimou, J. Llorens, V. Moreno, and A. Fraga, “A methodology for the classification of quality of requirements using machine learning techniques,” Information and Software Technology, vol. 67, pp. 180–195, 2015.
• J. Winkler and A. Vogelsang, “Automatic classification of requirements based on convolutional neural networks,” in 2016 IEEE 24th International Requirements Engineering Conference Workshops (REW), IEEE, 2016, pp. 39–45.
• F. Nazir, W. H. Butt, M. W. Anwar, and M. A. K. Khattak, “The applications of natural language processing (nlp) for software requirement engineering-a systematic literature review,” in International conference on information science and applications, Springer, 2017, pp. 485–493.
• A. Rossanez et al., “Semi-automatic checklist-based quality assessment of natural language requirements= avalia¸c˜ao semi-autom´atica de qualidade de requisitos em lıngua natural baseada em checklist,” 2017.
• F. Dalpiaz, A. Ferrari, X. Franch, and C. Palomares, “Natural language processing for requirements engineering: The best is yet to come,” IEEE software, vol. 35, no. 5, pp. 115–119, 2018.
• T. Iqbal, P. Elahidoost, and L. Lucio, “A bird’s eye view on requirements engineering and machine learning,” in 2018 25th Asia-Pacific Software Engineering Conference (APSEC), IEEE, 2018, pp. 11–20.
• A. Ferrari and A. Esuli, “An nlp approach for cross-domain ambiguity detection in requirements engineering,” Automated Software Engineering, vol. 26, no. 3, pp. 559–598, 2019.
• E. Dias Canedo and B. Cordeiro Mendes, “Software requirements classification using machine learning algorithms,” Entropy, vol. 22, no. 9, p. 1057, 2020.
• S. Panichella and M. Ruiz, “Requirements-collector: Automating requirements specification from elicitation sessions and user feedback,” in 2020 IEEE 28th International Requirements Engineering Conference (RE), IEEE, 2020, pp. 404– 407.
• N. Peinelt, D. Nguyen, and M. Liakata, “Tbert: Topic models and bert joining forces for semantic similarity detection,” in Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 2020, pp. 7047– 7055.
• L. Zhao, W. Alhoshan, A. Ferrari, K. J. Letsholo, M. A. Ajagbe, E.-V. Chioasca, and R. T. Batista-Navarro, “Natural language processing (nlp) for requirements engineering: A systematic mapping study,” arXiv preprint arXiv:2004.01099, 2020.