LikeThis! Empowering App Users to Submit UI Improvement Suggestions Instead of Complaints

TL;DR

This paper introduces LikeThis!, a GenAI-based system that transforms user feedback with screenshots into multiple UI improvement suggestions, enhancing feedback quality and facilitating better user-developer collaboration.

Contribution

We propose a novel AI approach that generates concrete UI improvement suggestions from user comments and screenshots, outperforming existing image models and improving feedback effectiveness.

Findings

GPT-Image-1 outperforms other models in UI design improvement

Generated suggestions increase feedback clarity and actionability

User study shows improved developer understanding of feedback

Abstract

User feedback is crucial for the evolution of mobile apps. However, research suggests that users tend to submit uninformative, vague, or destructive feedback. Unlike recent AI4SE approaches that focus on generating code and other development artifacts, our work aims at empowering users to submit better and more constructive UI feedback with concrete suggestions on how to improve the app. We propose LikeThis!, a GenAI-based approach that takes a user comment with the corresponding screenshot to immediately generate multiple improvement alternatives, from which the user can easily choose their preferred option. To evaluate LikeThis!, we first conducted a model benchmarking study based on a public dataset of carefully critiqued UI designs. The results show that GPT-Image-1 significantly outperformed three other state-of-the-art image generation models in improving the designs to address UI issues while keeping the fidelity and without introducing new issues. An intermediate step in LikeThis! is to generate a solution specification before sketching the design as a key to achieving effective improvement. Second, we conducted a user study with 10 production apps, where 15 users used LikeThis! to submit their feedback on encountered issues. Later, the developers of the apps assessed the understandability and actionability of the feedback with and without generated improvements. The results show that our approach helps generate better feedback from both user and developer perspectives, paving the way for AI-assisted user-developer collaboration.