Predicting Usefulness of Crowd Testing Reports with Deep Learning
Cai Jingxuan1,Wu Jiang1,2(),Wang Chengkun1
1School of Information Management, Wuhan University, Wuhan 430072, China 2Center for E-commerce Research and Development, Wuhan University, Wuhan 430072, China
[Objective] This paper tries to predict the usefulness of crowd testing reports with author attributes, text features, and image features. [Methods] First, we adopted deep learning techniques to extract text and image features from crowd testing reports. Then, we constrcuted a prediction model with full-connected neural network. Third, we trained the new model with 80% of samples and different input combinations. Finally, we examined our model’s performance with the remaining samples. [Results] With the help of text or image features, the prediction accuracy of the model increased by 4.24% and 5.21%, respectively. Using both the text and image features, our model’s prediction accuracy increased by 6.96%. [Limitations] The extracted features of texts and images were not understandable and interpretable. Therefore, we cannot identify specific features represented by each layer of neural network in the model. [Conclusions] The proposed model with text and image features can effectively predict the usefulness of crowd testing reports.
蔡婧璇,吴江,王诚坤. 基于深度学习的众测报告有用性预测研究*[J]. 数据分析与知识发现, 2020, 4(11): 102-111.
Cai Jingxuan,Wu Jiang,Wang Chengkun. Predicting Usefulness of Crowd Testing Reports with Deep Learning. Data Analysis and Knowledge Discovery, 2020, 4(11): 102-111.
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