Abstract
System requirement specification (SRS) documents specify the client’s requirements and specifications in the software systems. Requirement engineering is a mandatory phase of the Software Development Life Cycle (SDLC) that includes defining, documenting, and maintaining system requirements. As the complexity increases it becomes difficult to categorize and prioritize the various Software Requirements (SR). Different natural language processing methods such as tokenization, lemmatization is used in the text pre-processing phase followed by Term frequency-inverse document frequency (TF-IDF). The aim of this research is to compare existing Machine Learning algorithms to evaluate which algorithm is able to efficiently classify the system requirements. The algorithms are assessed on two parameters precision and accuracy. The results obtained showed Decision Tree (DT) could identify all types of requirements except portability. But the accuracy of Support Vector Machine (SVM) is highest with 78.57% for the publicly available dataset than that of DT which has an accuracy of 61.42%
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Apte, S., Honrao, Y., Shinde, R., Talele, P., Phalnikar, R. (2023). Automatic Extraction of Software Requirements Using Machine Learning. In: Choudrie, J., Mahalle, P.N., Perumal, T., Joshi, A. (eds) ICT with Intelligent Applications. ICTIS 2023. Lecture Notes in Networks and Systems, vol 719. Springer, Singapore. https://doi.org/10.1007/978-981-99-3758-5_33
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DOI: https://doi.org/10.1007/978-981-99-3758-5_33
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