Duplicate Bug Report Detection Using an Attention-Based Neural Language Model

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...

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Bibliographic Details
Published in:IEEE transactions on reliability 2023-06, Vol.72 (2), p.846-858
Main Authors: Messaoud, Montassar Ben, Miladi, Asma, Jenhani, Ilyes, Mkaouer, Mohamed Wiem, Ghadhab, Lobna
Format: Article
Language:English
Subjects:
Accuracy
Artificial neural networks
Bidirectional encoder representations from ransformers (BERT)
Bit error rate
Coders
Computational modeling
Computer bugs
Computer Science
Context
Debugging
deep learning
duplicate bug report
Feature extraction
Multilayer perceptrons
pretrained neural language model
Reproduction (copying)
Semantics
Software
Task analysis
Tracking systems
Transfer learning
Unstructured data
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Summary: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.
ISSN:0018-9529
1558-1721
DOI:10.1109/TR.2022.3193645