In this paper, we conduct an empirical study to analyze the performance variations among 35 deep learning-based automated bug assignment approaches.
Wang et al. [50] evaluated the impact of five word embedding techniques, namely Word2Vec [31], GloVe [35], NextBug [13], ELMo [36], and BERT [10], on the bug�...
6 days ago � 王荣存,wangrongcun,中国矿业大学主页平台系统, An empirical assessment of different word embedding and deep learning models for bug assignment
An empirical assessment of different word embedding and deep learning models for bug assignment (2024), JSS, Wang, Rongcun, et al. On Extracting Specialized�...
Jul 25, 2024 � We studied the efficacy of five widely recognized WE models for six BRM tasks on 20 widely-used products from the Eclipse and Mozilla�...
The paper outlines the effectiveness of word2vec technique on the efficiency of classifiers to predict a bug severity from a bug report and examines the impact�...
Jun 1, 2023 � An empirical assessment of different word embedding and deep learning models for bug assignment ... assignment: Ensemble-based machine learning�...
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Jul 31, 2024 � We studied the efficacy of five widely recognized WE models for six BRM tasks on 20 widely-used products from the Eclipse and Mozilla foundations.
A deep learning method for bug triaging by considering design flaws is presented. The number of bad smells in the fixed software code is used for prediction.
Jun 1, 2023 � An accuracy of 75% (or more) was achieved for datasets with a sufficient number of samples for deep learning-based model training. A model is�...
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