Authors: Jialiang Wei, Anne-Lise Courbis, Thomas Lambolais, Binbin Xu, Pierre Louis Bernard, Gérard Dray, Walid Maalej
Abstract: Over the past decade, app store (AppStore)-inspired requirements elicitation
has proven to be highly beneficial. Developers often explore competitors’ apps
to gather inspiration for new features. With the advance of Generative AI,
recent studies have demonstrated the potential of large language model
(LLM)-inspired requirements elicitation. LLMs can assist in this process by
providing inspiration for new feature ideas. While both approaches are gaining
popularity in practice, there is a lack of insight into their differences. We
report on a comparative study between AppStore- and LLM-based approaches for
refining features into sub-features. By manually analyzing 1,200 sub-features
recommended from both approaches, we identified their benefits, challenges, and
key differences. While both approaches recommend highly relevant sub-features
with clear descriptions, LLMs seem more powerful particularly concerning novel
unseen app scopes. Moreover, some recommended features are imaginary with
unclear feasibility, which suggests the importance of a human-analyst in the
elicitation loop.
Source: http://arxiv.org/abs/2408.17404v1
