Supplementary materials of the paper entitled: "Generating Natural Language Requirements via Adversarial Examples in Deep Learning"

Supplementary materials of the paper entitled: "Generating Natural Language Requirements via Adversarial Examples in Deep Learning" File A: Datasets (.txt) A1: Webex A2: Zoom A3: Teams A4: Word A5: Power Point A6: Excel File B: Python Code (Both Ours and Baseline) B1: adversarial_samples.ipynb B2: Baseline.ipynb FIle C: Result Tables (.xlsx) C1: Table of Perturbed outputs in WebEx C2: Table of Perturbed outputs in Zoom C3: Table of Perturbed outputs in Teams C4: Table of Perturbed outputs in Word C5: Table of Perturbed outputs in Power Point C6: Table of Perturbed outputs in Excel (Excel) File D: Trend of Adversarial Shifts (Graphs) D1: Adversarial shifts of office suit (LSTM) D2: Adversarial shifts of video conferencing suit (LSTM) D3: Non-Adversarial shifts of office suit (LSTM) D4: Non-Adversarial shifts of video conferencing suit(LSTM) D5: Adversarial shifts of office suit (GRU) D6: Adversarial shifts of video conferencing suit (GRU) D7: Non-Adversarial shifts of office suit (GRU) D8: Non-Adversarial shifts of video conferencing suit(GRU) D9: Adversarial shifts of office suit (Bi-LSTM) D10: Adversarial shifts of video conferencing suit (Bi-LSTM) D11: Non-Adversarial shifts of office suit (Bi-LSTM) D12: Non-Adversarial shifts of video conferencing suit(Bi-LSTM) File E: Adversarial Examples (.pdf) E1: Adversarial vs Original in WebEx E2: Adversarial vs Original in Zoom E3: Adversarial vs Original in Teams E4: Adversarial vs Original in Word E5: Adversarial vs Original in Power Point E6: Adversarial vs Original in Excel File F: Questionnaire