ABSTRACT Introduction Organizations moving from monolithic to microservice architectures face new challenges due to distributed complexity. Architectural Anti‐Patterns (Smells) can arise and contribute to Technical Debt, while current detection approaches remain manual or semi‐automated and prone to error. Methods This study aims to address this gap by proposing an automated detection tool leveraging graph neural networks (GNNs). Microservice systems are modeled as graphs, with services as nodes and their relationships as edges. Graph neural networks (GNNs) are applied to detect four anti‐patterns: (1) cyclic dependencies, (2) enterprise service bus (ESB) usage, (3) microservice greediness, and (4) inappropriate service intimacy. Large language models (LLMs) are used to generate and expand architectural graph datasets to address data scarcity. Results The GNN approach achieves improved detection performance, with up to a 1.2% F1‐score increase over existing tools such as Msanose and Arcan. Conclusion Combining GNNs with LLM‐augmented data enhances automated detection of microservice anti‐patterns and supports more effective architectural assessment.

Abstract In the current state of the video game productions, most of the video game levels are created by the human operators working as level designers. This manual process is not only time-consuming and resource-intensive but also hard to guarantee uniform quality in the contents created by the level designers. One way to address this issue is to use computer-assisted level design techniques. In this paper, we have proposed a novel framework for computer-assisted video game level design that leverages neural networks, particularly generative adversarial networks (GANs) and autoencoders. The general idea is to learn over a dataset of high-quality levels and subsequently improve the ones created by the level designers. The proposed method is independent of the graphical dimensionality of the game and will work for 2D and 3D games in general. The autoencoder is used to create an intermediate representation of the level that is itself changed using the backpropagation technique according to the feedback obtained by feeding the output of the autoencoder to the discriminator component of the GAN. After performing a series of evaluations on the proposed framework and by automatically improving a series of purposefully corrupted game levels, the results demonstrate a noticeable improvement compared with the usage of simple autoencoders used to improve the video game levels in the previous researches.

In trust management systems, the trustor should be able to select a trustee candidate that has the maximum trustworthiness degree toward a specific goal and an amount of risk consistent with her/his risk acceptance degree. In this research, a novel computational trust model based on the principles of uncertainty theory is introduced. In the proposed model, trust is considered to be constructed of trustworthiness components. To calculate each of these trustworthiness components, empirical distributions of recommenders and trustor’s opinions about the existing trustworthiness and risk degrees of the trustee candidates are aggregated. In the decision making stage, the trustee candidate with the optimum trustworthiness and risk degrees is selected according to uncertain goal programming. Based on this method, trustworthiness and risk degrees of the trustee candidates are calculated according to the amount of negative and positive deviations from the optimal state. To verify the accuracy of the model’s behavior, a series of simulation scenarios are constructed. The results of these simulations demonstrate that the proposed model effectively selects the best trustee candidate according to parameters such as context, priorities of the trustworthiness components, trustor’s constraints and the trustworthiness and risk acceptance degrees. Finally, by comparing the model with other commonly used computational trust modeling approaches, it is shown that the proposed model has a lower mean absolute error (MAE) and produces more accurate results.