JITGNN: A deep graph neural network framework for Just-In-Time bug prediction-Reference-Cited by-同舟云学术

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JITGNN: A deep graph neural network framework for Just-In-Time bug prediction

Published:2024-01 Issue: Volume: Page:111984
ISSN:0164-1212
Container-title:Journal of Systems and Software
language:en
Short-container-title:Journal of Systems and Software

Author:

Keshavarz Hossein,Rodríguez-Pérez Gema^ORCID

Funder

NSERC

Publisher

Elsevier BV

Reference81 articles.

1. Suggesting accurate method and class names;Allamanis,2015

2. Learning to represent programs with graphs, CoRR abs/1711.00740;Allamanis,2017

3. code2seq: Generating sequences from structured representations of code;Alon,2019

4. Code2vec: Learning distributed representations of code;Alon;Proc. ACM Program. Lang.,2019

5. Predicting fault-prone components in a java legacy system;Arisholm,2006

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1. Cross-project concurrency bug prediction using domain-adversarial neural network;Journal of Systems and Software;2024-08

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