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Creativity in requirements engineering (RE) has recently emerged to help innovate “novel and useful” requirements and improve a software system’s sustainability. Existing research has mostly focused on workshops, techniques, and tools to aid creative requirements elicitation. Limited attention, however, has been dedicated to creativity evaluation, despite the current mechanisms being largely restricted to rating requirements for a broad notion of “novelty and appropriateness”. In addition, such mechanisms focus on evaluating creativity from an elicitation perspective, leaving other RE activities widely disregarded. To further advance the literature, we present a preliminary study on developing a framework that aims to evaluate creativity in a precise manner and accounts for the full spectrum of RE activities. In particular, we propose a “creative requirement diagnosis scale (CRDS)” that includes 27 indicators to assess creativity, present a novel framework to evaluate the creative merits of requirements in terms of the complete RE process, and further evaluate requirements using our framework in a study with 53 participants. The results suggest our framework’s potential to capture creativity aspects that would otherwise be undetected by traditional techniques. Our study also indicates the need for further refinement of the framework, thereby opening new avenues for creativity in RE.
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Prompting Creative Requirements via Traceable and Adversarial Examples in Deep Learning
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.
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- Award ID(s):
- 2236953
- PAR ID:
- 10507750
- Publisher / Repository:
- IEEE
- Date Published:
- ISBN:
- 979-8-3503-2689-5
- Page Range / eLocation ID:
- 134 to 145
- Subject(s) / Keyword(s):
- creative requirements automated requirements generation deep learning adversarial examples
- Format(s):
- Medium: X
- Location:
- Hannover, Germany
- Sponsoring Org:
- National Science Foundation
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