Jingjing Liao
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Jingjing Liao has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
- Lei Xiao
College of Computer and Information Engineering, Xiamen University of Technology, Xiamen 361024, China
1 shared publication - Xiao-Zhu Xie
Xiamen University of Technology
1 shared publication - Xiaodan Zhou
Xiamen University of Technology, China
1 shared publication - Yihao Li
Xiamen University of Technology, China
1 shared publication
Latest Publications
- Open Access
- 1 January 2024
- Published by IGI Global Scientific Publishing in International Journal of Web Services Research
- Vol. 21 (1) , 1-19
Clustering and Prioritization of Web Crowdsourced Test Reports Based on Text Classification
In crowdsourced testing, prioritizing numerous test reports is critical for improving developer review efficiency. Many researchers have proposed methods for prioritizing crowdsourced test reports for mobile applications, however, web crowdsourced test reports usually contain more detailed text and more complex screenshots, which makes prioritizing them especially necessary. This paper proposes a prioritization method named TCDiv, based on the data characteristics of web crowdsourcing test reports. First, using a pre-trained TextCNN model, the text of each report is segmented into two parts: reproduction steps and defect descriptions. Then, features are extracted from textual and image information respectively, and clustering technique is utilized to classify similar reports, and finally the clustering results are ranked and sampled. To validate the approach, experiments were conducted on 717 web crowdsourced test reports. The results show that TCDiv can detect more different defects in a limited time, thus improving the review efficiency of the development team.
- 1 January 2024
- Published by IGI Global Scientific Publishing in International Journal of Web Services Research
- Vol. 21 (1) , 1-19
Open AccessClustering and Prioritization of Web Crowdsourced Test Reports Based on Text Classification
In crowdsourced testing, prioritizing numerous test reports is critical for improving developer review efficiency. Many researchers have proposed methods for prioritizing crowdsourced test reports for mobile applications, however, web crowdsourced test reports usually contain more detailed text and more complex screenshots, which makes prioritizing them especially necessary. This paper proposes a prioritization method named TCDiv, based on the data characteristics of web crowdsourcing test reports. First, using a pre-trained TextCNN model, the text of each report is segmented into two parts: reproduction steps and defect descriptions. Then, features are extracted from textual and image information respectively, and clustering technique is utilized to classify similar reports, and finally the clustering results are ranked and sampled. To validate the approach, experiments were conducted on 717 web crowdsourced test reports. The results show that TCDiv can detect more different defects in a limited time, thus improving the review efficiency of the development team.