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One- and Two-Phase Software Requirement Classification Using Ensemble Deep Learning.

Nouf Rahimi1,2, Fathy Eassa1, Lamiaa Elrefaei3

  • 1Computer Science Department, Faculty of Computing and Information Technology, King Abdul-Aziz University, Jeddah 21589, Saudi Arabia.

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|October 23, 2021
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Summary
This summary is machine-generated.

Deep learning ensemble methods effectively classify software requirements. A two-phase approach achieved 95.7% accuracy in binary classification and 93.4% in multi-class classification, outperforming single-phase systems.

Keywords:
BiLSTMCNNGRULSTMclassificationdeep learningensemblefunctional requirementnon-functional requirementsoftware requirement

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Area of Science:

  • Software Engineering
  • Artificial Intelligence
  • Machine Learning

Background:

  • Deep learning (DL) shows promise in automating software engineering (SE) tasks.
  • Ensemble approaches in DL aim to enhance performance and reduce errors.
  • Classifying software requirements (SRs) is crucial for SE.

Purpose of the Study:

  • To apply and evaluate three DL ensemble methods for software requirement classification.
  • To compare a one-phase vs. a two-phase classification system for SRs.
  • To assess the performance of DL models including LSTM, BiLSTM, GRU, and CNN.

Main Methods:

  • Utilized three ensemble techniques: accuracy-weighted, mean, and accuracy-per-class weighted ensembles.
  • Combined four DL models: LSTM, BiLSTM, GRU, and CNN.
  • Developed one-phase and two-phase classification systems for SRs on the PROMISE dataset.

Main Results:

  • The two-phase classification system achieved 95.7% accuracy for binary (FR/NFR) classification and 93.4% for multi-class (17 classes) classification.
  • Both proposed systems demonstrated competitive performance against state-of-the-art methods.
  • The two-phase system showed superior robustness compared to the one-phase system.

Conclusions:

  • Deep learning ensemble methods offer a robust solution for software requirement classification.
  • The two-phase classification strategy is more effective for complex SR classification tasks.
  • The study highlights the potential of DL in advancing automated software engineering.