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