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/

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