SiameseQAT: A Semantic Context-Based Duplicate Bug Report Detection Using Replicated Cluster Information

SiameseQAT: A Semantic Context-Based Duplicate Bug Report Detection Using Replicated Cluster Information

In large-scale software development environments, defect reports are maintained through bug tracking systems (BTS) and analyzed by domain experts. Different users may create bug reports in a non-standard manner and may report a particular problem using a particular set of words due to stylistic choi...

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Bibliographic Details
Main Authors: Thiago Marques Rocha, Andre Luiz Da Costa Carvalho
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
Duplicate bug report
deep learning
deep neural networks
semantic context-based
Siamese network
loss function
Online Access:https://ieeexplore.ieee.org/document/9380447/

Internet

https://ieeexplore.ieee.org/document/9380447/

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