Cognitive Complexity and Graph Convolutional Approach Over Control Flow Graph for Software Defect Prediction

Cognitive Complexity and Graph Convolutional Approach Over Control Flow Graph for Software Defect Prediction

The software engineering community is working to develop reliable metrics to improve software quality. It is estimated that understanding the source code accounts for 60% of the software maintenance effort. Cognitive informatics is important in quantifying the degree of difficulty or the...

Full description

Bibliographic Details
Main Authors:	Mansi Gupta, Kumar Rajnish, Vandana Bhattacharjee
Format:	Article
Language:	English
Published:	IEEE 2022-01-01
Series:	IEEE Access
Subjects:	Basic control structures cognitive complexity measures control flow graphs graph convolutional network neural network software defect prediction
Online Access:	https://ieeexplore.ieee.org/document/9916268/

Similar Items

Research of Software Defect Prediction Model Based on Complex Network and Graph Neural Network
by: Mengtian Cui, et al.
Published: (2022-09-01)

Multi-Channel Graph Convolutional Networks for Graphs with Inconsistent Structures and Features
by: Xinglong Chang, et al.
Published: (2024-02-01)

BugPre: an intelligent software version-to-version bug prediction system using graph convolutional neural networks
by: Zixu Wang, et al.
Published: (2022-08-01)

A Network Science Perspective of Graph Convolutional Networks: A Survey
by: Mingshan Jia, et al.
Published: (2023-01-01)

WMGCN: Weighted Meta-Graph Based Graph Convolutional Networks for Representation Learning in Heterogeneous Networks
by: Jinli Zhang, et al.
Published: (2020-01-01)

Topological Graph Convolutional Network Based on Complex Network Characteristics
by: He Gao, et al.
Published: (2022-01-01)

A comprehensive review of graph convolutional networks: approaches and applications
by: Xinzheng Xu, et al.
Published: (2023-05-01)

Time-Evolving Graph Convolutional Recurrent Network for Traffic Prediction
by: Weimin Mai, et al.
Published: (2022-03-01)

Graph Convolutional Networks Guided by Explicitly Estimated Homophily and Heterophily Degree
by: Rui Zhang, et al.
Published: (2022-10-01)

ERGCN: Enhanced Relational Graph Convolution Network, an Optimization for Entity Prediction Tasks on Temporal Knowledge Graphs
by: Yinglin Wang, et al.
Published: (2022-12-01)

MSGCN: Multi-Subgraph Based Heterogeneous Graph Convolution Network Embedding
by: Junhui Chen, et al.
Published: (2021-10-01)

Graph Convolutional Network Design for Node Classification Accuracy Improvement
by: Mohammad Abrar Shakil Sejan, et al.
Published: (2023-08-01)

High-Order Topology-Enhanced Graph Convolutional Networks for Dynamic Graphs
by: Jiawei Zhu, et al.
Published: (2022-10-01)

Interpreting Deep Graph Convolutional Networks with Spectrum Perspective
by: Sisi Zhang, et al.
Published: (2023-05-01)

Poisson kernel: avoiding self-smoothing in graph convolutional networks
by: Yang, Ziqing, et al.
Published: (2022)

An Efficient Graph Convolutional RVFL Network for Hyperspectral Image Classification
by: Zijia Zhang, et al.
Published: (2023-12-01)

Detection of fake news campaigns using graph convolutional networks
by: Dimitrios Michail, et al.
Published: (2022-11-01)

Merit: multi-level graph embedding refinement framework for large-scale graph
by: Weishuai Che, et al.
Published: (2023-08-01)

A novel Knowledge Graph recommendation algorithm based on Graph Convolutional Network
by: Hui Guo, et al.
Published: (2024-12-01)

Stress and Corrosion Defect Identification in Weak Magnetic Leakage Signals Using Multi-Graph Splitting and Fusion Graph Convolution Networks
by: Shaoxuan Zhang, et al.
Published: (2023-01-01)

Research on Virus Morphology Recognition Method Based on Enhanced Graph Convolutional Network
by: Yan HA, Weicheng YUAN, Xiangjie MENG, Junfeng TIAN
Published: (2022-05-01)

Multi-Order-Content-Based Adaptive Graph Attention Network for Graph Node Classification
by: Yong Chen, et al.
Published: (2023-05-01)

Improving Variational Graph Autoencoders With Multi-Order Graph Convolutions
by: Lining Yuan, et al.
Published: (2024-01-01)

Hyperspectral Image Classification with Localized Graph Convolutional Filtering
by: Shengliang Pu, et al.
Published: (2021-02-01)

Kolmogorov Basic Graphs and Their Application in Network Complexity Analysis
by: Amirmohammad Farzaneh, et al.
Published: (2021-11-01)

A Dynamic Convolutional Network-Based Model for Knowledge Graph Completion
by: Haoliang Peng, et al.
Published: (2022-03-01)

Android Malware Detection Based on Behavioral-Level Features with Graph Convolutional Networks
by: Qingling Xu, et al.
Published: (2023-11-01)

Prediction of Water Leakage in Pipeline Networks Using Graph Convolutional Network Method
by: Ersin Şahin, et al.
Published: (2023-06-01)

IntME: Combined Improving Feature Interactions and Matrix Multiplication for Convolution-Based Knowledge Graph Embedding
by: Haonan Zhang, et al.
Published: (2023-08-01)

Heterogeneous Multi-Behavior Recommendation Based on Graph Convolutional Networks
by: Ran Rang, et al.
Published: (2023-01-01)

Graph Neural Networks: A bibliometrics overview
by: Abdalsamad Keramatfar, et al.
Published: (2022-12-01)

Graph Neural Network for Traffic Forecasting: The Research Progress
by: Weiwei Jiang, et al.
Published: (2023-02-01)

Malicious Powershell Detection Using Graph Convolution Network
by: Sunoh Choi
Published: (2021-07-01)

GCNCPR-ACPs: a novel graph convolution network method for ACPs prediction
by: Xiujin Wu, et al.
Published: (2022-12-01)

Relational graph convolutional networks: a closer look
by: Thiviyan Thanapalasingam, et al.
Published: (2022-11-01)

Information cascade prediction of complex networks based on physics-informed graph convolutional network
by: Dingguo Yu, et al.
Published: (2024-01-01)

Graph Convolutional Network for Drug Response Prediction Using Gene Expression Data
by: Seonghun Kim, et al.
Published: (2021-04-01)

Graph Convolution Network over Dependency Structure Improve Knowledge Base Question Answering
by: Chenggong Zhang, et al.
Published: (2023-06-01)

Convolutional Neural Network Outperforms Graph Neural Network on the Spatially Variant Graph Data
by: Anna Boronina, et al.
Published: (2023-05-01)

Dual-channel deep graph convolutional neural networks
by: Zhonglin Ye, et al.
Published: (2024-04-01)