Accurate and robust ambiguity detection in software requirements documents using a GloVe-BiLSTM deep learning framework with data augmentation.

Requirements engineering is an important part of software production, maintenance, and development. Requirements engineering documents are typically written in natural language, which is prone to uncertainties, and identification of these ambiguities is essential for successful software development. Nevertheless, categorizing and identifying these uncertainties by human experts is both time-consuming and expensive. Automating this procedure improves precision and lowers costs. This research proposes a novel deep learning-based ambiguity detection framework, addressing a critical challenge in software development. The proposed framework relies on GloVe word embeddings and a Bidirectional Long Short-Term Memory (Bi-LSTM) model. The reason for choosing the Bi-LSTM model is its ability to model sequential dependencies and contextual relationships in natural language. GloVe embeddings were selected because of their ability to effectively capture semantic similarities in text, which is essential for comprehending ambiguous language in requirements specifications. Our approach also incorporates data augmentation using back translation to mitigate the class imbalance by generating diverse samples of the minority class and improving the model's generalizability. In addition to the proposed GloVe_BiLSTM model, we conducted an ablation study to evaluate the impact of different components by comparing it with alternative configurations, including GloVe_CNN, GloVe_CNN_RNN_LSTM, and GloVe_LSTM. The experimental dataset was a publicly available Fault-Prone SRS dataset, which contains 7061 data points across six ambiguity classes, namely, Lexical, Syntactic, Semantic, Syntax, Pragmatic, and Clean. This study provided insights into the contributions of each component, with GloVe_BiLSTM outperforming other models, achieving over 90% accuracy and superior performance across precision, recall, and F1-score metrics. Specifically, GloVe_BiLSTM attained a precision of 92.04%, recall of 91.52%, F1-score of 91.65%, and an accuracy of 92.06%. These results demonstrate the robustness and efficacy of the proposed approach, highlighting its potential for enhancing ambiguity detection in software requirements. The proposed approach emphasizes interpretability by identifying specific ambiguous patterns in requirements, making it practical for real-world applications. This method not only improves efficiency in requirements engineering but also offers a scalable solution with significant potential for reducing costs and increasing the reliability of requirements specification in software projects.