Title: Research on the application of clustering algorithm based on hybrid similarity in high precision information processing of crowdsourcing test reports

Authors: Li Huang; Xin Zheng

Addresses: Information Engineering College, Jiangxi University of Technology, Nanchang, 330098, China ' Artificial Intelligence Department, Jiangxi University of Technology, Nanchang, 330098, China

Abstract: Crowdsourcing testing is an emerging method that improves software product quality by quickly identifying and rectifying defects. This study optimises the application of clustering algorithms in crowdsourcing testing to enhance test report review efficiency. It demonstrates that the best defect detection is achieved at a 0.4 threshold, and a 0.7 weight yields the optimal harmonic average. The clustering effect is best when text information is primary, and screenshot information is secondary. The proposed model improves upon traditional models by 9.336%. The high-precision information processing algorithm proposed herein considers the interrelationships in test reports, thereby improving the accuracy of the clustering algorithm. This enhances the work efficiency of auditors, testing quality, and reduces testing costs.

Keywords: crowdsourcing testing; image segmentation; text segmentation; hybrid similarity; clustering algorithm.

DOI: 10.1504/IJCSYSE.2024.142771

International Journal of Computational Systems Engineering, 2024 Vol.8 No.3/4, pp.248 - 256

Received: 24 Mar 2023
Accepted: 11 Jun 2023
Published online: 21 Nov 2024 *

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