# Read It, Don't Watch It: Captioning Bug Recordings Automatically

arXiv: https://arxiv.org/abs/2302.00886 · HTML: https://mlsanity.com/paper/2302.00886/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Feb 2023.

## Built on by (most cited first, 3 of 3)

- [Agent for User: Testing Multi-User Interactive Features in TikTok](https://mlsanity.com/paper/2504.15474/) (2025)
- [Semantic GUI Scene Learning and Video Alignment for Detecting Duplicate Video-based Bug Reports](https://mlsanity.com/paper/2407.08610/) (2024)
- [Breaking Single-Tester Limits: Multi-Agent LLMs for Multi-User Feature Testing](https://mlsanity.com/paper/2506.17539/) (2025)

## Abstract (arXiv)

Screen recordings of mobile applications are easy to capture and include a wealth of information, making them a popular mechanism for users to inform developers of the problems encountered in the bug reports. However, watching the bug recordings and efficiently understanding the semantics of user actions can be time-consuming and tedious for developers. Inspired by the conception of the video subtitle in movie industry, we present a lightweight approach CAPdroid to caption bug recordings automatically. CAPdroid is a purely image-based and non-intrusive approach by using image processing and convolutional deep learning models to segment bug recordings, infer user action attributes, and generate subtitle descriptions. The automated experiments demonstrate the good performance of CAPdroid in inferring user actions from the recordings, and a user study confirms the usefulness of our generated step descriptions in assisting developers with bug replay.