CTRAS: a tool for aggregating and summarizing crowdsourced test reports

In this paper, we present CTRAS, a tool for automatically aggregating and summarizing duplicate crowdsourced test reports on the fly. CTRAS can automatically detect duplicates based on both textual information and the screenshots, and further aggregates and summarizes the duplicate test reports. CTRAS provides end users with a comprehensive and comprehensible understanding of all duplicates by identifying the main topics across the group of aggregated test reports and highlighting supplementary topics that are mentioned in subgroups of test reports. Also, it provides the classic tool of issue tracking systems, such as the project-report dashboard and keyword searching, and automates their classic functionalities, such as bug triaging and best fixer recommendation, to assist end users in managing and diagnosing test reports. Video: https://youtu.be/PNP10gKIPFs

[1]  Thomas Zimmermann,et al.  Duplicate bug reports considered harmful … really? , 2008, 2008 IEEE International Conference on Software Maintenance.

[2]  Tao Zhang,et al.  PRST: A PageRank-Based Summarization Technique for Summarizing Bug Reports with Duplicates , 2017, Int. J. Softw. Eng. Knowl. Eng..

[3]  Yang Feng,et al.  CTRAS: Crowdsourced Test Report Aggregation and Summarization , 2019, 2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE).

[4]  Tao Xie,et al.  An approach to detecting duplicate bug reports using natural language and execution information , 2008, 2008 ACM/IEEE 30th International Conference on Software Engineering.

[5]  Tao Zhang,et al.  Bug Reports for Desktop Software and Mobile Apps in GitHub: What's the Difference? , 2019, IEEE Software.

[6]  Siau-Cheng Khoo,et al.  A discriminative model approach for accurate duplicate bug report retrieval , 2010, 2010 ACM/IEEE 32nd International Conference on Software Engineering.

[7]  He Jiang,et al.  Mining authorship characteristics in bug repositories , 2014, Science China Information Sciences.

[8]  Yang Feng,et al.  Multi-objective test report prioritization using image understanding , 2016, 2016 31st IEEE/ACM International Conference on Automated Software Engineering (ASE).