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.
Figures & tables
Figure 1. Overview of LikeThis! . A user reports an app issue along with a screenshot (1). The AI pipeline generates n solution specifications (2), which are used to create improvement suggestions (3). The user selects their preferred suggestion (4) and submits it along with the issue to developers (5).
Figure 2. LikeThis! iOS app enables users to report issues by capturing a screenshot, describing their issue, and optionally marking an area (Screens 1–2). Here, the user reports accidentally tapping the call button while entering a number. LikeThis! generates improvement suggestions (Screens 3–4). Users can then select a suggestion to submit, edit, or reject all. In this case, the user chooses to change the call button to a Slide-To-Call control and submits it with an optional comment (Screen 5).
Figure 3. Data sampling from UICrit dataset for RQ1.
Figure 4. Evaluation tool to assess generated UIs.
F#1
Resolution
Fidelity
Robustness
Frequency
Avg
Frequency
Avg
Frequency
Avg
1
2
3
1
2
3
1
2
3
GPT
214
22
18
200
2.74
4
33
203
2.83
1
27
212
2.88
Flux
14
153
29
58
1.60
4
30
206
2.84
11
70
159
2.62
Gemini
8
114
53
73
1.83
18
43
179
2.67
35
115
90
2.23
Bagel
4
181
27
32
1.38
9
19
212
2.85
100
35
105
2.02
Table 1. Results of 240 annotations (by 2 annotators for 120 tasks) comparing four models.
F#1
Resolution
Fidelity
Robustness
Frequency
Avg
Frequency
Avg
Frequency
Avg
1
2
3
1
2
3
1
2
3
S
Mask
28
6
4
30
2.60
0
0
40
3.00
1
10
29
2.70
No mask
12
8
2
30
2.55
0
4
36
2.90
0
5
35
2.88
M
Mask
21
0
2
38
2.95
5
2
33
2.70
1
7
32
2.77
No mask
19
1
2
37
2.90
2
6
32
2.75
0
3
37
2.92
Table 2. Results of 120 annotations (by 2 annotators for 60 tasks) comparing GPT-Image-1 with and without mask.
F#1
Resolution
Fidelity
Robustness
Frequency
Avg
Frequency
Avg
Frequency
Avg
1
2
3
1
2
3
1
2
3
Mask
SG
108
10
10
100
2.75
5
26
89
2.70
2
29
89
2.73
N-SG
12
78
7
35
1.64
66
11
43
1.81
61
22
37
1.80
No mask
SG
80
10
11
99
2.74
1
12
107
2.88
0
11
109
2.91
N-SG
40
25
13
82
2.48
3
6
111
2.90
3
16
101
2.82
Table 3. Results of 120 annotations (by 2 annotators for 60 tasks) comparing GPT-Image-1-mask with and without “Suggestion Generation”, and 120 annotations (by 2 annotators for 60 tasks) comparing GPT-Image-1 with and without “Suggestion Generation”.
Users
Id
Gender
Age
Occupation
U1
M
28
Dentist
U2
M
19
Pediatric caregiver
U3
M
27
Marketing consultant
U4
F
26
Teacher
U5
F
28
Kindergarten teacher
Table 4. Participants in user and developer studies.
Rating
Count
Percentage
Very inaccurate
0
0%
Rather inaccurate
0
0%
Somewhat accurate
5
6.9%
Mostly accurate
22
30.6%
Very accurate
41
56.9%
Canceled submission
4
5.6%
Table 5. “How accurate is your selected suggestion in addressing the issue you wanted to report?” (User testing)
Response
Count
Percentage
Strongly disagree
0
0%
Disagree
1
7.1%
Undecided
1
7.1%
Agree
5
35.7%
Strongly agree
8
57.1%
Total
15
100%
Table 6. “When encountering issues with the apps you regularly use, do you think this feedback approach (with suggestions to select from) will help you to submit your complaints and feedback?” (User testing)
Before seeing the improvement suggestion
Count (%)
Not Understandable
5 (7.4%)
Somewhat Understandable
15 (22.1%)
Clearly Understandable
48 (70.6%)
Total
68 (100%)
Table 7. Understandability of user feedback before and after seeing improvement suggestions. (Developer survey)
Response
A
B
Yes, I could directly implement a change the suggestion
40 (58.8%)
48 (70.6%)
Possibly, but I would need more information
15 (22.1%)
17 (25.0%)
No, I don’t know what I should do
13 (19.1%)
3 (4.4%)
Total
68 (100%)
Table 8. Actionability of the textual feedback (A) and the associated improvement suggestions (B). (Developer survey)