An Intelligent Prototype for Requirements Validation Process Using Machine Learning Algorithms
Hadeel Al Qaisi, Gaith Y Quba, Ahmad Althunibat, Ayman Mahmoud Aref Abdalla, Shadi Mahmoud Faleh AlZu’bi · 2021
with the advancement of technology, the world is growing and developing rapidly, and the demand for software has become greater and more, and one of the most important steps in building any program is the requirements of the programs, and checking the requirements when applying them manually, requires great effort, time, cost and accuracy. Automated requirements were not sufficient. In this study, we will propose a technique for automatically checking software requirements by using machine learning to represent textual data from software requirements specifications, an overview of prototyping-based models in machine learning is presented in this paper. The framework, notes, i.e. data, are stored in terms of typical reps. the system can be used in conjunction with an appropriate similarity scale in the context of the unsupervised analysis of high-dimensional complex datasets. Supervised learning is represented in prototyping systems in terms of vector quantization learning. In most cases, The familiar Euclidean distance serves as a measure of difference. We present framework extensions to non-standard measures give an introduction to use adaptive distances in related learning, Briefly, the prototype is less costly from any other technical for validating the requirement because it make in the first and the prototype too can reuse and customer involvement