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Accurate and robust ambiguity detection in software requirements documents using a GloVe-BiLSTM deep learning

Younes Abdeahad1, Mahdi Kabootari1, Yalda Kheirkhah1

  • 1Department of Computer Engineering, Ma.C., Islamic Azad University, Mashhad, Iran.

Scientific Reports
|April 24, 2026
PubMed
Summary

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This summary is machine-generated.

This study introduces a deep learning framework using GloVe word embeddings and a Bidirectional Long Short-Term Memory (Bi-LSTM) model to automatically detect ambiguities in software requirements. The novel approach achieved over 90% accuracy, improving efficiency and reliability in software development.

Area of Science:

  • Software Engineering
  • Natural Language Processing
  • Artificial Intelligence

Background:

  • Requirements engineering documents are often written in natural language, leading to ambiguities that hinder software development.
  • Manual identification of these ambiguities is time-consuming, expensive, and prone to human error.
  • Automating ambiguity detection is crucial for improving precision and reducing costs in software projects.

Purpose of the Study:

  • To propose and evaluate a novel deep learning-based framework for automated ambiguity detection in software requirements.
  • To leverage GloVe word embeddings and a Bidirectional Long Short-Term Memory (Bi-LSTM) model for enhanced natural language understanding.
  • To address class imbalance and improve model generalizability through data augmentation techniques.

Main Methods:

Keywords:
Ambiguity detectionBidirectional long short-term memoryData augmentationDeep learningRequirements engineering

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  • Developed a deep learning framework utilizing GloVe word embeddings and a Bi-LSTM model.
  • Employed back translation for data augmentation to mitigate class imbalance.
  • Conducted an ablation study comparing GloVe_BiLSTM with GloVe_CNN, GloVe_CNN_RNN_LSTM, and GloVe_LSTM models.
  • Evaluated the framework on the publicly available Fault-Prone SRS dataset with six ambiguity classes.

Main Results:

  • The proposed GloVe_BiLSTM model achieved over 90% accuracy, outperforming alternative configurations.
  • Superior performance was demonstrated across precision (92.04%), recall (91.52%), and F1-score (91.65%).
  • The framework effectively identified specific ambiguous patterns, enhancing interpretability.

Conclusions:

  • The GloVe_BiLSTM framework offers a robust and effective solution for automated ambiguity detection in software requirements.
  • The approach significantly improves efficiency, scalability, and reliability in requirements engineering.
  • This method has practical potential for reducing costs and enhancing the quality of software specifications.