SemCluster: a semi-supervised clustering tool for crowdsourced test reports with deep image understanding
Abstract
Due to the openness of crowdsourced testing, mobile app crowdsourced testing has been subject to duplicate reports. The previous research methods extract the textual features of the crowdsourced test reports, combine with shallow image analysis, and perform unsupervised clustering on the crowdsourced test reports to clarify the duplication of crowdsourced test reports and solve the problem. However, these methods ignore the semantic connection between textual descriptions and screenshots, making the clustering results unsatisfactory and the deduplication effect less accurate.
BibTeX
@inproceedings{Du-al:FSE22,
author = {Mingzhe Du and
Shengcheng Yu and
Chunrong Fang and
Tongyu Li and
Heyuan Zhang and
Zhenyu Chen},
title = {{SemCluster:} a semi-supervised clustering tool for crowdsourced test reports with deep image understanding},
booktitle = {{ESEC/SIGSOFT} {FSE}},
pages = {1756--1759},
publisher = {{ACM}},
year = {2022},
}