Code Graph Research Articles

Density of identifying codes of hexagonal grids with finite number of rows

In a graph G, a set C ⊆ V (G) is an identifying code if, for all vertices v in G, the sets N[v] ∩ C are all nonempty and pairwise distinct, where N[v] denotes the closed neighbourhood of v. We focus on the minimum density of identifying codes of infinite hexagonal grids Hk with k rows, denoted by d*(Hk), and present optimal solutions for k ≤ 5. Using the discharging method, we also prove a lower bound in terms of maximum degree for the minimum-density identifying codes of well-behaved infinite graphs. We prove that d*(H2) = 9/20, d*(H3) = 6/13 ≈ 0.4615, d*(H4) = 7/16 = 0.4375 and d*(H5) = 11/25 = 0.44. We also prove that H2 has a unique periodic identifying code with minimum density.

A multi-type vulnerability detection framework with parallel perspective fusion and hierarchical feature enhancement

A core problem of vulnerability detection is to detect multi-type vulnerabilities simultaneously by characterizing vulnerabilities of high diversity and complexity in real program source code. Current methods mainly adjust and compromise multiple code representations such as code sequence and code graph based on composite graph. However, sequential features extracted by graph are hardly sufficient to model the contextual semantic associations of the token sequence. Meanwhile, structural features of the code graph extracted by models based on Euclidean Graph Neural Network are difficult to fit the tree-like calling relationships between code lines. These limitations make it difficult to detect diverse vulnerabilities. In addition, most of the existing models ignore the type of code statement, which is closely associated with some specific vulnerability types. In this paper, we propose a Parallelism Framework with Hierarchical feature Enhancement for Multi-type Vulnerability Detection (PFHE-MVD). PFHE-MVD models program code from three parallel perspectives, containing sequence, code graph, and Abstract Syntax Tree statistic. Hyperbolic Graph Convolutional Neural Network is integrated to model the top-down hierarchical calling structure in program code graph through hyperbolic space mapping. Besides, the statement type of code is embedded along with the code text to strengthen the identification ability for different types of vulnerabilities. Experimental results show that PFHE-MVD achieves new state-of-the-art results in multi-type vulnerability detection. PFHE-MVD captures tree-like hierarchical code structure feature and enhances the distinguishing ability for vulnerabilities by code statement type embedding.

Context and Multi-Features-Based Vulnerability Detection: A Vulnerability Detection Frame Based on Context Slicing and Multi-Features.

With the increasing use of open-source libraries and secondary development, software projects face security vulnerabilities. Existing studies on source code vulnerability detection rely on natural language processing techniques, but they overlook the intricate dependencies in programming languages. To address this, we propose a framework called Context and Multi-Features-based Vulnerability Detection (CMFVD). CMFVD integrates source code graphs and textual sequences, using a novel slicing method called Context Slicing to capture contextual information. The framework combines graph convolutional networks (GCNs) and bidirectional gated recurrent units (BGRUs) with attention mechanisms to extract local semantic and syntactic information. Experimental results on Software Assurance Reference Datasets (SARDs) demonstrate CMFVD's effectiveness, achieving the highest F1-score of 0.986 and outperforming other models. CMFVD offers a promising approach to identifying and rectifying security flaws in large-scale codebases.

On connected components and perfect codes of proper order graphs of finite groups

On connected components and perfect codes of proper order graphs of finite groups

The Perfect Codes of Non-coprime and Coprime Graphs

In this paper, we focus on the perfect and total perfect codes of the non-coprime and coprime graphs associated to the dihedral groups and finite Abelian groups. We used the advantage of independent sets and tried to present the independent polynomial for them.

Semantic Code Graph—An Information Model to Facilitate Software Comprehension

Semantic Code Graph—An Information Model to Facilitate Software Comprehension

Neighbour-transitive codes in Kneser graphs

A code C is a subset of the vertex set of a graph and C is s-neighbour-transitive if its automorphism group Aut(C) acts transitively on each of the first s+1 parts C0,C1,…,Cs of the distance partition{C=C0,C1,…,Cρ}, where ρ is the covering radius of C. While codes have traditionally been studied in the Hamming and Johnson graphs, we consider here codes in the Kneser graphs. Let Ω be the underlying set on which the Kneser graph K(n,k) is defined. Our first main result says that if C is a 2-neighbour-transitive code in K(n,k) such that C has minimum distance at least 5, then n=2k+1 (i.e., C is a code in an odd graph) and C lies in a particular infinite family or is one particular sporadic example. We then prove several results when C is a neighbour-transitive code in the Kneser graph K(n,k). First, if Aut(C) acts intransitively on Ω we characterise C in terms of certain parameters. We then assume that Aut(C) acts transitively on Ω, first proving that if C has minimum distance at least 3 then either K(n,k) is an odd graph or Aut(C) has a 2-homogeneous (and hence primitive) action on Ω. We then assume that C is a code in an odd graph and Aut(C) acts imprimitively on Ω and characterise C in terms of certain parameters. We give examples in each of these cases and pose several open problems.

Fine-grained smart contract vulnerability detection by heterogeneous code feature learning and automated dataset construction

Context:Recently, several deep learning based smart contract vulnerability detection approaches have been proposed. However, challenges still exist in applying deep learning for fine-grained vulnerability detection in smart contracts, including the lack of the dataset with sufficient statement-level labeled smart contract samples and neglect of heterogeneity between syntax and semantic features during code feature learning. Objective:To utilize deep learning for fine-grained smart contract vulnerability detection, we propose a security best practices (SBP) based dataset construction approach to address the scarcity of datasets. Moreover, we propose a syntax-sensitive graph neural network to address the challenge of heterogeneous code feature learning. Method:The dataset construction approach is motivated by the insight that smart contract code fragments guarded by security best practices may contain vulnerabilities in their original unguarded code form. Thus, we locate and strip security best practices from the smart contract code to recover its original vulnerable code form and perform sample labeling. Meanwhile, as the heterogeneity between tree-structured syntax features embodied inside the abstract syntax tree (AST) and graph-structured semantic features reflected by relations between statements, we propose a code graph whose nodes are each statement’s AST subtree with a syntax-sensitive graph neural network that enhances the graph neural network by a child-sum tree-LSTM cell to learn these heterogeneous features for fine-grained smart contract vulnerability detection. Results:We compare our approach with three state-of-the-art deep learning-based approaches that only support contract-level vulnerability detection and two popular static analysis-based approaches that support fine detection granularity. The experiment results show that our approach outperforms the baselines at both coarse and fine granularities. Conclusion:In this paper, we propose utilizing security best practices inside the smart contract code to construct the dataset with statement-level labels. To learn both tree-structured syntax and graph-structured semantic code features, we propose a syntax-sensitive graph neural network. The experimental results show that our approach outperforms the baselines.

Tensor‐based gated graph neural network for automatic vulnerability detection in source code

AbstractThe rapid expansion of smart devices leads to the increasing demand for vulnerability detection in the cyber security field. Writing secure source codes is crucial to protect applications and software. Recent vulnerability detection methods are mainly using machine learning and deep learning. However, there are still some challenges, how to learn accurate source code semantic embedding at the function level, how to effectively perform vulnerability detection using the learned semantic embedding of source code and how to solve the overfitting problem of learning‐based models. In this paper, we consider codes as various graphs with node features and propose a tensor‐based gated graph neural network called TensorGNN to produce code embedding for function‐level vulnerability detection. First, we propose a high‐dimensional tensor for combining different code graph representations. Second, inspired by the work of tensor technology, we propose the TensorGNN model to produce accurate code representations using the graph tensor. We evaluate our model on 7 C and C++ large open‐source code corpus (e.g. SARD&NVD, Debian, SATE IV, FFmpeg, libpng&LibTiff, Wireshark and Github datasets), which contains 13 types of vulnerabilities. Our TensorGNN model improves on existing state‐of‐the‐art works by 10%–30% on average in terms of vulnerability detection accuracy and F1, while our TensorGNN model needs less training time and model parameters. Specifically, compared with other existing works, our model reduces 25–47 times of the number of parameters and decreases 3–10 times of training time. Results of evaluations show that TensorGNN has better performance while using fewer training parameters and less training time.

End-to-end programmable computing systems

Recent technological advances have contributed to the rapid increase in algorithmic complexity of applications, ranging from signal processing to autonomous systems. To control this complexity and endow heterogeneous computing systems with autonomous programming and optimization capabilities, we propose a unified, end-to-end, programmable graph representation learning (PGL) framework that mines the complexity of high-level programs down to low-level virtual machine intermediate representation, extracts specific computational patterns, and predicts which code segments run best on a core in heterogeneous hardware. PGL extracts multifractal features from code graphs and exploits graph representation learning strategies for automatic parallelization and correct assignment to heterogeneous processors. The comprehensive evaluation of PGL on existing and emerging complex software demonstrates a 6.42x and 2.02x speedup compared to thread-based execution and state-of-the-art techniques, respectively. Our PGL framework leads to higher processing efficiency, which is crucial for future AI and high-performance computing applications such as autonomous vehicles and machine vision.

CCCS: Contrastive Cross-Language Code Search Using Code Graph Information

Developers often search and reuse existing code snippets to improve software development efficiency during software development. Currently, researchers have proposed many code search methods. However, the search intent of existing methods is basically a natural language query. In order to support code migration and code refactoring, it is necessary to search relevant code snippets of another programming language with code snippets of one programming language. In this paper, we propose a C ontrastive C ross-language C ode S earch method using code graph information, called CCCS . CCCS first converts code snippets into high-dimensional vectors using pre-trained CodeBERT to extract the sequence features of code snippets. Next, the structural features of code snippets are extracted using a graph convolutional neural network. Finally, the model is trained using the contrastive learning method to optimize the vector representation of cross-language code snippets, enabling the model to distinguish code snippets from different programming languages with the same functionality. To evaluate the effectiveness of our method, we conducted comparison experiments and ablation experiments on a small-scale dataset and a large-scale dataset, respectively. The experimental results show that our method far outperforms the state-of-the-art baseline model in terms of MRR metrics.

KAMPNet: multi-source medical knowledge augmented medication prediction network with multi-level graph contrastive learning

BackgroundsPredicting medications is a crucial task in intelligent healthcare systems, aiding doctors in making informed decisions based on electronic medical records (EMR). However, medication prediction faces challenges due to complex relations within heterogeneous medical data. Existing studies primarily focus on the supervised mining of hierarchical relations between homogeneous codes in medical ontology graphs, such as diagnosis codes. Few studies consider the valuable relations, including synergistic relations between medications, concurrent relations between diseases, and therapeutic relations between medications and diseases from historical EMR. This limitation restricts prediction performance and application scenarios.MethodsTo address these limitations, we propose KAMPNet, a multi-sourced medical knowledge augmented medication prediction network. KAMPNet captures diverse relations between medical codes using a multi-level graph contrastive learning framework. Firstly, unsupervised graph contrastive learning with a graph attention network encoder captures implicit relations within homogeneous medical codes from the medical ontology graph, generating knowledge augmented medical code embedding vectors. Then, unsupervised graph contrastive learning with a weighted graph convolutional network encoder captures correlative relations between homogeneous or heterogeneous medical codes from the constructed medical codes relation graph, producing relation augmented medical code embedding vectors. Finally, the augmented medical code embedding vectors, along with supervised medical code embedding vectors, are fed into a sequential learning network to capture temporal relations of medical codes and predict medications for patients.ResultsExperimental results on the public MIMIC-III dataset demonstrate the superior performance of our KAMPNet model over several baseline models, as measured by Jaccard, F1 score, and PR-AUC for medication prediction.ConclusionsOur KAMPNet model can effectively capture the valuable relations between medical codes inherent in multi-sourced medical knowledge using the proposed multi-level graph contrastive learning framework. Moreover, The multi-channel sequence learning network facilitates capturing temporal relations between medical codes, enabling comprehensive patient representations for downstream tasks such as medication prediction.

Enhancing intelligent IoT services development by integrated multi-token code completion

The Internet of Things (IoT) is a revolutionary network of interconnected devices embedded with sensors and software that enables seamless communication, data sharing, and intelligent decision-making in the form of IoT services. To facilitate the efficient development of IoT services, code completion technique provides a promising solution by providing suggestions for missing code snippets. The development trend of IoT services is to support more mobile device terminals. Mobile devices are portable and easy to use, allowing IoT device operation and management anytime and anywhere. However, the current multi-token completion methods struggle to guarantee code generation quality under the constraints of low resources and low latency, making it difficult to fully support IoT service development. We propose a multi-token code completion framework, S2RCC, which completes code from skeleton to refinement with dual encoder and dual decoder. The framework consists of two phases: first, the code skeleton, which is the simplification of code containing structure-sensitive tokens, is predicted based on the semantics of the code context; second, the broken context is repaired with the predicted skeleton, and then parsed into the code structure so that the specific tokens can be generated combining the semantics and structure of context. Furthermore, we then provide an implementation of the framework, representing the repaired code as an improved Heterogeneous code graph and fusing the semantics and structure of code context by the three-layer stacked attention. We conducted experiments on multi-token completion datasets, showing that our model has achieved the state-of-the-art with the smallest possible scale and the fastest generation speed.

Summarizing source code through heterogeneous feature fusion and extraction

Code summarization, which seeks to automatically produce a succinct natural-language description to summarize the functionality of source code, plays an essential role in maintaining the software. Currently, plentiful approaches have been proposed to first encode the source code based on its Abstract Syntax Tree (AST), and then decode it into a textual summary. However, most existing works interpret the AST-based syntax structure as a homogeneous graph, without discriminating the different relations between graph nodes (e.g., the parent–child and sibling relations) in a heterogeneous way. To mitigate this issue, this paper proposes HetCoS to extract the syntactic and sequential features of source code by exploring its inherent heterogeneity for code summarization. Specifically, we first build a Heterogeneous Code Graph (HCG) that fuses the syntax structure and code sequence with eight types of edges/relations designed between graph nodes. Moreover, we present a heterogeneous graph neural network for capturing the diverse relations in HCG. The represented HCG is then fed into a Transformer decoder, followed by a multi-head attention-based copying mechanism to support high-quality summary generation. Extensive experiments on the major Java and Python datasets illustrate the superiority of our approach over sixteen state-of-the-art baselines. To promote reproducibility studies, we make the implementation of HetCoS publicly accessible at https://github.com/GJCEXP/HETCOS.

Perfect codes in proper intersection power graphs of finite groups

Perfect codes in proper intersection power graphs of finite groups

The Adjacency Codes of the First Yellow Graphs

The Adjacency Codes of the First Yellow Graphs

FaaSLight : General Application-level Cold-start Latency Optimization for Function-as-a-Service in Serverless Computing

Serverless computing is a popular cloud computing paradigm that frees developers from server management. Function-as-a-Service (FaaS) is the most popular implementation of serverless computing, representing applications as event-driven and stateless functions. However, existing studies report that functions of FaaS applications severely suffer from cold-start latency. In this article, we propose an approach, namely, FaaSLight , to accelerating the cold start for FaaS applications through application-level optimization. We first conduct a measurement study to investigate the possible root cause of the cold-start problem of FaaS. The result shows that application code loading latency is a significant overhead. Therefore, loading only indispensable code from FaaS applications can be an adequate solution. Based on this insight, we identify code related to application functionalities by constructing the function-level call graph and separate other code (i.e., optional code) from FaaS applications. The separated optional code can be loaded on demand to avoid the inaccurate identification of indispensable code causing application failure. In particular, a key principle guiding the design of FaaSLight is inherently general, i.e., platform - and language-agnostic . In practice, FaaSLight can be effectively applied to FaaS applications developed in different programming languages (Python and JavaScript), and can be seamlessly deployed on popular serverless platforms such as AWS Lambda and Google Cloud Functions, without having to modify the underlying OSes or hypervisors, nor introducing any additional manual engineering efforts to developers. The evaluation results on real-world FaaS applications show that FaaSLight can significantly reduce the code loading latency (up to 78.95%, 28.78% on average), thereby reducing the cold-start latency. As a result, the total response latency of functions can be decreased by up to 42.05% (19.21% on average). Compared with the state-of-the-art, FaaSLight achieves a 21.25× improvement in reducing the average total response latency.

HGAT: smart contract vulnerability detection method based on hierarchical graph attention network

With the widespread use of blockchain, more and more smart contracts are being deployed, and their internal logic is getting more and more sophisticated. Due to the large false positive rate and low detection accuracy of most current detection methods, which heavily rely on already established detection criteria, certain smart contracts additionally call for human secondary detection, resulting in low detection efficiency. In this study, we propose HGAT, a hierarchical graph attention network-based detection model, in order to address the aforementioned issues as well as the shortcomings of current smart contract vulnerability detection approaches. First, using Syntax Tree (AST) and Control Flow Graph, the functions in the smart contract are abstracted into code graphs (CFG). Then abstract each node in the code subgraph, extract the node features, utilize the graph attention mechanism GAT, splice the obtained vectors to form the features of each line of statements and use these features to detect smart contracts. To create test data and assess HGAT, we leverage the open-source smart contract vulnerability sample dataset. The findings of the experiment indicate that this method can identify smart contract vulnerabilities more quickly and precisely than other detection techniques.

Q_Graph Data and Code

Q_Graph Data and Code

DeepVulSeeker: A novel vulnerability identification framework via code graph structure and pre-training mechanism

Software vulnerabilities can pose severe harms to a computing system. They can lead to system crash, privacy leakage, or even physical damage. Correctly identifying vulnerabilities among enormous software codes in a timely manner is so far the essential prerequisite to patch them. Unfortunately, the current vulnerability identification methods, either the classic ones or the deep-learning-based ones, have several critical drawbacks, making them unable to meet the present-day demands put forward by the software industry. To overcome the drawbacks, in this paper, we propose DeepVulSeeker, a novel fully automated vulnerability identification framework, which leverages both code graph structures and the semantic features with the help of the recently advanced Graph Representation Self-Attention and pre-training mechanisms. Our experiments show that DeepVulSeeker not only reaches an accuracy as high as 0.99 on traditional CWE datasets, but also outperforms all other existing methods on two highly-complicated datasets. We also testified DeepVulSeeker based on three case studies, and found that DeepVulSeeker is able to understand the implications of the vulnerabilities. We have fully implemented DeepVulSeeker and open-sourced it for future follow-up research.