Prioritisation of mobile crowdsourced test reports based on text analytics

Long Lin; Lei Xiao; Hanghai Shi

doi:10.1117/12.3038201

7 August 2024 Prioritisation of mobile crowdsourced test reports based on text analytics

Long Lin, Lei Xiao, Hanghai Shi

Proceedings Volume 13229, Seventh International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2024); 1322911 (2024) https://doi.org/10.1117/12.3038201
Event: Seventh International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2024), 2024, Nanchang, China

Abstract

Crowdsourced testing is a useful way to quickly identify defects and save time. However, the quality of crowdsourced testing reports varies widely since most testers are non-professionals. Effectively reviewing these test reports has become an urgent issue. To address this issue, researchers have proposed various techniques for classifying, clustering, and prioritising reports. These methods often directly extract text-related features without further understanding the textual content. By delving deeper into the textual information of crowdsourced testing reports, sentences within the text descriptions can be categorised into two types: defect descriptions and step descriptions. This paper presents a prioritisation method (TPLSI) for crowdsourced testing reports to enhance the efficiency of report review. Initially, a pre-trained LSTM is employed to classify the textual part of the crowdsourced testing reports. Subsequently, extract textual and visual features from the reports, merge these features, and use clustering techniques to group similar reports. Finally, the inspection sequence of the testing reports is determined by considering the severity of the issues reported in the crowdsourced testing reports. Furthermore, the experiments in this study were conducted on industrial crowdsourced projects from six different domains. The results indicate that the proposed method is capable of detecting errors more quickly within a limited timeframe.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Long Lin, Lei Xiao, and Hanghai Shi "Prioritisation of mobile crowdsourced test reports based on text analytics", Proc. SPIE 13229, Seventh International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2024), 1322911 (7 August 2024); https://doi.org/10.1117/12.3038201

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KEYWORDS

Feature extraction

Image fusion

Defect detection

Feature fusion

Neural networks

Principal component analysis

Scanning probe microscopy

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