INFERENCE ON UNSEEN DATA RELATED TO FUNCTION/NON-FUNCTIONAL REQUIREMENT CLASSIFICATION
Users can upload a csv file of functional and non-functional requirements in a format similar to benchmark datasets (see download section).
On successful activation of processing command, exploratory data analysis engine will process the data shortly in order to predict functional or non-functional class labels against requirement text.
User will be able to download the result file after data processing by clicking on the button.
TRAINING THE MODEL FROM SCRATCH
Users need to provide a csv file containing multi-class requirements in a format similar to benchmark datasets (see download section).
User has the freedom to choose data split method.
User has the freedom to choose number of folds for data split.
User has the freedom to choose number of epochs.
User has the freedom to choose batch size.
User has the freedom to choose learning rate.
Sign up preferably using an organizational email account with required data and purpose of experimentation.
After completing the Sign-Up process, users need to wait for account approval and training permission.
If the request is approved, users can log in for one-time training.
On successful activation of the processing command, the exploratory model training engine will process the data shortly to train the model.
At the end of training, users can download performance-related artifacts to analyze model behavior.