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

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arxiv 2302.00886 v1 pith:7LRBQTLF submitted 2023-02-02 cs.SE

Sidong Feng , Mulong Xie , Yinxing Xue , Chunyang Chen This is my paper · ORCID
classification cs.SE
keywords recordingsusercapdroiddevelopersactionsapproachautomaticallydescriptions
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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