Semantic Representation Based on AMR Graph for Document Level Question Generation

Cheng, Shun; Chen, Jinyan; Yu, Yongxin

doi:10.1145/3578741.3578762

Cited by 2 publications

(6 citation statements)

References 16 publications

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“…Therefore, it has been gradually applied to natural language processing tasks (i.e. text classification [28–30] and text generation [31–34]) and software engineering tasks (i.e. test case generation [35–37] and code summarisation [38–40]) in recent years.…”

Section: Related Workmentioning

confidence: 99%

Just‐in‐time defect prediction enhanced by the joint method of line label fusion and file filtering

Zhang

Kuang

et al. 2023

IET Software

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Just-In-Time (JIT) defect prediction aims to predict the defect proneness of software changes when they are initially submitted. It has become a hot topic in software defect prediction due to its timely manner and traceability. Researchers have proposed many JIT defect prediction approaches. However, these approaches cannot effectively utilise line labels representing added or removed lines and ignore the noise caused by defectirrelevant files. Therefore, a JIT defect prediction model enhanced by the joint method of line label Fusion and file Filtering (JIT-FF) is proposed. Firstly, to distinguish added and removed lines while preserving the original software changes information, the authors represent the code changes as original, added, and removed codes according to line labels. Secondly, to obtain semantics-enhanced code representation, a cross-attentionbased line label fusion method to perform complementary feature enhancement is proposed. Thirdly, to generate code changes containing fewer defect-irrelevant files, the authors formalise the file filtering as a sequential decision problem and propose a reinforcement learning-based file filtering method. Finally, based on generated code changes, CodeBERT-based commit representation and multi-layer perceptron-based defect prediction are performed to identify the defective software changes. The experiments demonstrate that JIT-FF can predict defective software changes more effectively.

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Section: Related Workmentioning

confidence: 99%

Just‐in‐time defect prediction enhanced by the joint method of line label fusion and file filtering

Zhang

Kuang

et al. 2023

IET Software

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show abstract

“…(BiGGNN). 14 Given a program graph G = (V, E), BiGGNN learns the node embeddings from both incoming and outgoing directions for the graph. In particular, each node v ∈ V is initialized by a learnable embedding matrix E and gets its initial representation h 0 v ∈ R d where d is the dimensional length.…”

Section: Definition 4 (Pdg) For a Programmentioning

confidence: 99%

“…Then, we use a dense layer for learning the classification and finally pass the data through the softmax layer to get the predicted labels. Since VulGraB aims to learn the semantics in the code, a weighted average is used as the aggregation function, 14 where the weights are derived from the normalized forward adjacency matrix A → and the backward adjacency matrix A ← . Figure 7 shows the specific algorithm process diagram for the bolded part in Figure 6.…”

Section: Bidirectional Gated Graph Neural Network Module Definitionmentioning

confidence: 99%

“…On the other hand, inspired by the above work related to deep learning, this article proposes the use of bi‐directional gated graph sequence neural network (BiGGNN) 14 to perform the task of code vulnerability detection. The main idea of BiGGNN is that based on the gated GNN model, a bi‐directional recurrent mechanism is added to the network model training and the node embedding is updated with gated recurrent units (GRU) to improve the model learning.…”

Section: Introductionmentioning

confidence: 99%

“…Definition (BiGGNN) 14 . Given a program graph

G=\left(V,E\right)

, BiGGNN learns the node embeddings from both incoming and outgoing directions for the graph.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

VulGraB: Graph‐embedding‐based code vulnerability detection with bi‐directional gated graph neural network

Wang

Chen

2023

Softw Pract Exp

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Code vulnerabilities can have serious consequences such as system attacks and data leakage, making it crucial to perform code vulnerability detection during the software development phase. Deep learning is an emerging approach for vulnerability detection tasks. Existing deep learning‐based code vulnerability detection methods are usually based on word2vec embedding of linear sequences of source code, followed by code vulnerability detection through RNNs network. However, such methods can only capture the superficial structural or syntactic information of the source code text, which is not suitable for modeling the complex control flow and data flow and miss edge information in the graph structure constructed by the source code, with limited effect of neural network model. To solve the above problems, this article proposes a code vulnerability detection method, named VulGraB, which is based on graph embedding and bidirectional gated graph neural networks. VulGraB uses node2vec to convert the program‐dependent graphs into graph embeddings of the code, which contain rich structure information of the source code, improving the ability of features to express nonlinear information to a certain extent. Then the BiGGNN is used for training, and finally the accuracy of the detection results is evaluated using target program. The bi‐directional gated neural network utilizes a bi‐directional recurrent structure, which is beneficial to global information aggregation. The experimental results show that the accuracy of VulGraB is significantly improved over the baseline models on two datasets, with F1 scores of 85.89% and 97.24% being the highest, demonstrating that VulGraB consistently outperforms other effective vulnerability detection models.

show abstract

Semantic Representation Based on AMR Graph for Document Level Question Generation

Cited by 2 publications

References 16 publications

Just‐in‐time defect prediction enhanced by the joint method of line label fusion and file filtering

Just‐in‐time defect prediction enhanced by the joint method of line label fusion and file filtering

VulGraB: Graph‐embedding‐based code vulnerability detection with bi‐directional gated graph neural network

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