Crowdsourced testing has been widely adopted to improve the quality of various software products. Crowdsourced workers typically perform testing tasks and report their experiences through test reports. While the crowdsourced test reports provide feedbacks from real usage scenarios, inspecting such a large number of reports becomes a time-consuming yet inevitable task. To improve the efficiency of this task, existing widely used issue-tracking systems, such as JIRA, Bugzilla, and Mantis, have provided keyword-search-based methods to assist users in identifying duplicate test reports. However, on mobile devices (such as mobile phones), where the crowdsourced test reports often contain insufficient text descriptions but instead rich screenshots, these text-analysis-based methods become less effective because the data has fundamentally changed. In this paper, instead of focusing on only detecting duplicates based on textual descriptions, we present CTRAS: a novel approach to leveraging duplicates to enrich the content of bug descriptions and improve the efficiency of inspecting these reports. CTRAS is capable of automatically aggregating duplicates based on both textual information and screenshots, and further summarizes the duplicate test reports into a comprehensive and comprehensible report. To validate CTRAS, we conducted quantitative studies using more than 5000 test reports, collected from 12 industrial crowdsourced projects. The experimental results reveal that CTRAS can reach an accuracy of 0.87, on average, regarding automatically detecting duplicate reports, and it outperforms the classic Max-Coverage-based and MMR summarization methods under Jensen Shannon divergence metric. Moreover, we conducted a task-based user study with 30 participants, whose result indicates that CTRAS can save nearly 30% time cost on average without loss of correctness.

@inproceedings{HaoFJL019,
  title = {CTRAS: Crowdsourced Test Report Aggregation and Summarization},
  author = {Rui Hao and Yang Feng and James A. Jones and Yuying Li and Zhenyu Chen},
  booktitle = {ICSE},
  year = {2019},
  doi = {10.1109/icse.2019.00096}
}