.png){kind=icon}
.png){kind=icon}
Return
Duplicate Bug Report Detection Using an Attention-Based Neural Language Model
.png){kind=icon}
PRE
AI
DOI：10.1109/TR.2022.3193645.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Context: Users and developers use bug tracking systems to report errors that occur during the development and testing of software. The manual identification of duplicates is a tedious task especially with software that have large bug repositories. In this context, their automatic detection becomes a necessary task that can help prevent frequently fixing the same bug. Objective: In this article, we propose BERT-MLP, a novel pretrained language model using bidirectional encoder representations from ransformers (BERT) for duplicate bug report detection (DBRD) with the aim of improving the detection rate compared to existing works. Method: Our approach considers only unstructured data. These are fed into the BERT model in order to learn the contextual relationships between words. The output is fed into a multilayer perceptron (MLP) classifier, representing our base DBRD. Results: Our approach was evaluated on three projects: Mozilla Firefox, Eclipse Platform, and Thunderbird. It achieved an accuracy of 92.11, 94.08, and 89.03%, respectively, for Mozilla, Eclipse, and Thunderbird. A comparison with a dual-channel convolutional neural network (DC-CNN) model and other pretrained models, including RoBERTa and Sentence-Bert has been conducted. Results showed that BERT-MLP outperformed, the second best performing models (DC-CNN and Sentence-BERT) by 12% in accuracy for Eclipse and 9% for both Mozilla and Thunderbird, respectively.
Keywords:
Computer bugs
Bit error rate
Task analysis
Feature extraction
Computational modeling
Transfer learning
Semantics
Bidirectional encoder representations from ransformers (BERT)
deep learning
duplicate bug report
pretrained neural language model
Journal
IF:
5.7
Papers:
2.7K
Citations:
8.5K
Organization
View more organizations