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

TL;DR

This paper introduces CAPdroid, a lightweight, image-based method that automatically captions bug recordings to help developers understand user actions more efficiently, reducing the time spent on bug analysis.

Contribution

CAPdroid is a novel non-intrusive approach that uses image processing and deep learning to generate descriptive subtitles for bug recordings, aiding developer comprehension.

Findings

CAPdroid accurately infers user actions from recordings.

Generated subtitles assist developers in bug replay tasks.

User study confirms usefulness of step descriptions.

Abstract

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.