# Getting Inspiration for Feature Elicitation: App Store- vs. LLM-based Approach

arXiv: https://arxiv.org/abs/2408.17404 · HTML: https://mlsanity.com/paper/2408.17404/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 57 references that are in the map.
- First posted on arXiv 30 Aug 2024.

## Builds on (most cited first, 14 of 14)

- [Advancing Requirements Engineering through Generative AI: Assessing the Role of LLMs](https://mlsanity.com/paper/2310.13976/) (2023)
- [Investigating ChatGPT's Potential to Assist in Requirements Elicitation Processes](https://mlsanity.com/paper/2307.07381/) (2023)
- [Elicitron: An LLM Agent-Based Simulation Framework for Design Requirements Elicitation](https://mlsanity.com/paper/2404.16045/) (2024)
- [LLM-based agents for automating the enhancement of user story quality: An early report](https://mlsanity.com/paper/2403.09442/) (2024)
- [Automatically Matching Bug Reports With Related App Reviews](https://mlsanity.com/paper/2102.07134/) (2021)
- [Release early, release often, and watch your users' emotions](https://mlsanity.com/paper/1906.06403/) (2019)
- [On the Automated Processing of User Feedback](https://mlsanity.com/paper/2407.15519/) (2024)
- [Gallery D.C.: Auto-created GUI Component Gallery for Design Search and Knowledge Discovery](https://mlsanity.com/paper/2204.06700/) (2022)
- [Unsupervised Topic Discovery in User Comments](https://mlsanity.com/paper/2108.08543/) (2021)
- [Strategies, Benefits and Challenges of App Store-inspired Requirements Elicitation](https://mlsanity.com/paper/2301.12090/) (2023)
- [On AI-Inspired UI-Design](https://mlsanity.com/paper/2406.13631/) (2024)
- [Zero-shot Bilingual App Reviews Mining with Large Language Models](https://mlsanity.com/paper/2311.03058/) (2023)
- [App Store 2.0: From Crowd Information to Actionable Feedback in Mobile Ecosystems](https://mlsanity.com/paper/1807.00518/) (2018)
- [Towards a Data-Driven Requirements Engineering Approach: Automatic Analysis of User Reviews](https://mlsanity.com/paper/2206.14669/) (2022)

## Built on by (most cited first, 1 of 1)

- [How Well Can AI Generate Backlogs from App Mockups?](https://mlsanity.com/paper/2607.22902/) (2026)

## Abstract (arXiv)

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.