• We propose ABG-NAS, an adaptive NAS framework for graph representation learning. • A novel genetic search strategy dynamically balances exploration and exploitation. • Bayesian optimization is embedded to tune hyperparameters during the search process. • Our method outperforms SOTA GNAS methods on four benchmark graph datasets. • ABG-NAS achieves high F1 scores on both sparse and dense real-world graph structures. Effective and efficient graph representation learning is essential for enabling critical downstream tasks, such as node classification, link prediction, and subgraph search. However, existing graph neural network (GNN) architectures often struggle to adapt to diverse and complex graph structures, limiting their ability to produce structure-aware and task-discriminative representations. To address this challenge, we propose ABG-NAS, a novel framework for automated graph neural network architecture search tailored for efficient graph representation learning. ABG-NAS encompasses three key components: a Comprehensive Architecture Search Space (CASS), an Adaptive Genetic Optimization Strategy (AGOS), and a Bayesian-Guided Tuning Module (BGTM). CASS systematically explores diverse propagation ( P ) and transformation ( T ) operations, enabling the discovery of GNN architectures capable of capturing intricate graph characteristics. AGOS dynamically balances exploration and exploitation, ensuring search efficiency and preserving solution diversity. BGTM further optimizes hyperparameters periodically, enhancing the robustness and scalability of the resulting architectures to both large-scale graphs and high-complexity models. Empirical evaluations on benchmark datasets (Cora, PubMed, Citeseer, and CoraFull) demonstrate that ABG-NAS consistently outperforms both manually designed GNNs and state-of-the-art neural architecture search (NAS) methods. These results highlight the potential of ABG-NAS to advance graph representation learning by providing adaptive solutions that scale effectively across varying graph sizes and architectural complexities. Our code is publicly available at https://github.com/sserranw/ABG-NAS .

ABSTRACT Introduction Microservices architecture is one of the most popular design approaches in software development. The granularity smell of microservices is a topic of great interest, which often leads to a degradation in the quality of the microservices architecture, so it needs to be eliminated through architecture refactoring. Existing research on architecture refactoring to address granularity smells in microservices is limited, with a lack of consideration for the semantic information of business logic, and suggestions for microservice refactoring rely too much on manual experience and lack standardized descriptions. Objectives This paper aims to provide a novel approach for refactoring granularity smells in microservice architectures, addressing the existing shortcomings in considering semantic information of microservice business logic, the reliance on empirical experience for refactoring suggestions, and the lack of standardized suggestions for refactoring. Methods This paper introduces a novel method for refactoring granularity smells in microservices architecture based on business topic clusters, named ASRMG. This method extracts business topic clusters from the business logic code of interfaces, thereby defining the cohesion and coupling semantics of the system. It then employs an enhanced genetic algorithm for refactoring the microservices system, using a refactoring pattern database to automate the generation of fine‐grained refactoring suggestions. Results Experiments conducted on five open‐source microservices systems of varying scales and domains achieved a 99.24% elimination rate of granularity smells, with cohesion metrics improving by an average of 60.19% and coupling metrics reducing by an average of 15.32%, indicating a significant enhancement in the quality of microservice architecture. Conclusion This paper introduces a novel method for refactoring and evaluating microservice systems, named ASRMG. ASRMG extracts business topic clusters from the code of microservice systems and refines the evaluation method for refactored microservice, and proposes an automated method for generating refactoring suggestions for granularity smells, enhancing the efficiency and quality of architecture smell refactoring.

In recent years, researchers have proposed various approaches to detect code comments that explicitly acknowledge Technical Debt (TD), which are referred to as Self-Admitted Technical Debt (SATD) comments. Previous studies have proven that hidden patterns can be learned from SATD code snippets to predict whether the code snippets hold TD without the aid of comments. In this study, we refer to such TD as unadmitted TD, i.e., TD whose code snippets exhibit patterns similar to those of SATD, but are not annotated with comments indicating the existence of TD. Given that current unadmitted TD datasets are limited to method-level and conditional-statement-level code snippets, we construct the world’s most comprehensive dataset of code snippets and their corresponding comments, which includes 18 popular Java open-source projects and covers code snippets at the file, class, method and block levels. Around this dataset, we have conducted four key research activities.Firstly, we propose an automated framework for data collection and annotation, which extracts commented code snippets of varying granularity from projects and assigns SATD labels using three state-of-the-art SATD detection approaches. Secondly, we conduct a rigorous evaluation process, including the validity test, reliability test and manual verification, to ensure the accuracy and consistency of the dataset before further analysis and utilization. Additionally, we propose a metric-based detection approach named LiteM that detects unadmitted TD solely based on code metrics. As for the real-world scenarios where training data is scarce, we further introduce LiteMC, which generates pseudo-labels for commented code snippets and then employs LiteM to train a model on these pseudo-labeled data, enabling the detection of unadmitted TD in uncommented code snippets. The experimental results demonstrate the effectiveness and efficiency of both LiteM and LiteMC. The dataset and the code are available athttps://github.com/HduDBSI/Dataset4TD.

Abstract 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.

In this paper, we discuss how Cellular Discrete Event System Specification (Cell-DEVS) theory can be used in modeling and simulation of the crowd. We will show that the efficient cell update mechanism of Cell-DEVS allows for more efficient entity-based simulation of the crowd compared to cellular automata. On the other hand the formal interfacing mechanisms provided by this theory allows for integration of other components such as DEVS atomic processing component or visualization and building information modeling components with the Cell-DEVS model. Finally, we describe in details of the design and development of several pedestrian models and present the results.