Optimizing Deep Learning Models for Detecting Ambiguities in Software Requirements: Harnessing <scp>BERT</scp> , Random Search, and Bi‐ <scp>LSTM</scp>

ABSTRACT Requirements engineering is one of the most crucial parts of the lifecycle of software engineering. Many programs fail annually due to deficiencies in requirements engineering. Requirements engineering documents are written in natural languages, which can lead to ambiguities. The presence of ambiguity in natural language causes misunderstandings. Accurate and timely identification of these requirements is vital for the development process. However, manual classification is time‐consuming and necessitates automation. Today, with the rapid advancement of technology, machine learning and deep learning are being used to detect these ambiguities in requirement specification documents. The BERT word embedding technique and the Bi‐LSTM algorithm were used in this research. We have used meta‐heuristic algorithms to choose the best value of hyperparameters of our deep learning algorithm. The publicly available Fault‐Prone SRS dataset was utilized to train the models. This dataset was also used to evaluate the performance of the proposed algorithm in terms of F1‐score, accuracy, and other statistical metrics. The BERT‐BiLSTM model outperformed other models in classifying and detecting ambiguities in requirement specification documents, achieving an F1‐score and higher than 81% accuracy.