Prompting Creative Requirements via Traceable and Adversarial Examples in Deep Learning
Hemanth Gudaparthi, Nan Niu, Boyang Wang, Tanmay Bhowmik, Hui Liu, Jianzhang Zhang, Juha Erik Savolainen, Glen Horton, Sean Crowe, Thomas Scherz, Lisa Haitz · 2023
Creativity focuses on the generation of novel and useful ideas. In this paper, we propose an approach to automatically generating creative requirements candidates via the adversarial examples resulted from applying small changes (perturbations) to the original requirements descriptions. We present an architecture where the perturbator and the classifier positively influence each other. Meanwhile, we ensure that each adversarial example is uniquely traceable to an existing feature of the software, instrumenting explainability. Our experimental evaluation of six datasets shows that around 20% adversarial shift rate is achievable. In addition, a human subject study demonstrates our results are more clear, novel, and useful than the requirements candidates outputted from a state-of-the-art machine learning method. To connect the creative requirements closer with software development, we collaborate with a software development team and show how our results can support behavior-driven development for a web app built by the team.