In the era of dynamic technological development and growing needs for data processing and analysis, the architecture of multi-agent systems is gaining importance. These systems, combined with Large Language Models (LLMs), offer an innovative approach to the information retrieval process that can enhance the efficiency, speed, and reliability of fact-finding and question-answering. This paper proposes the use of a designed multi-agent system architecture that uses autonomous agents and LLM models to efficiently acquire and process large amounts of data. It is shown how the integration of these technologies allows for more effective and precise information acquisition, which can lead to innovative solutions in both business and science. The effectiveness of solution were evaluated using specific metrics and testing.
Microservice architecture has emerged as the dominant architectural pattern for modern software systems, offering scalability, modularity, and fault tolerance. However, its highly distributed and interdependent nature makes evaluating architectural designs and proposed changes increasingly complex. This paper presents MicroViSim, a lightweight tool for simulating load and visualizing service dependencies and performance metrics in Kubernetes-based microservice systems. MicroViSim enables developers and operators to pre-evaluate new or evolving architectures prior to deployment by generating service dependency graphs, performance indicators, and load simulation results from user-defined YAML configurations. These visual insights help uncover potential bottlenecks and performance risks early in the design phase, thereby reducing future maintenance costs and the likelihood of system failures. Experiments using a real-world microservice application, Bookinfo, demonstrate that MicroViSim effectively identifies architectural weaknesses and supports informed deployment decisions.
Augmented Reality (AR) applications increasingly permeate domains such as gaming, retail, education, and healthcare. Despite their rapid adoption, systematic testing of AR apps remains underexplored due to their dependence on complex realworld contexts, sensor data, and diverse user interactions. In this paper, we propose ARTRIP (Automatic AR Testing with Randomized Interaction Patterns), a novel testing technique designed to explore AR applications with randomized interaction patterns. Unlike existing random testing approaches such as Monkey, ARTRIP uses a randomized interaction pattern to enhance the chance of covering more complicated interaction sequences. We describe the methodology, present a prototype implementation, and evaluate its effectiveness through case studies on four popular AR apps. Results suggest that ARTRIP achieves higher coverage than Monkey, highlighting its potential as a practical AR testing framework.
Proactive self-adaptation using Model Predictive Control (MPC) is well studied for software systems operating in dynamic environments. However, its practical adoption is limited by two key challenges. First, the modeling gap: traditional MPC requires state-space models that are difficult to construct for complex software systems, often demanding extensive domain expertise and incurring high computational costs. Second, the tuning gap: configuring MPC to balance abstract and competing objectives-such as performance, resource efficiency, and control stability-typically relies on manual, expert-driven tuning, impeding autonomous operation. To address these challenges, we propose a dual-layer MPC framework. At the lower layer, an Input Convex Neural Network (ICNN) is used to learn complex nonlinear dynamics directly from data, enabling tractable optimization and reducing the modeling burden. At the upper layer, a delta-driven controller manager adaptively tunes the lowerlayer MPC by monitoring deviations in system behavior and estimating the impact of different cost components on overall utility, thereby automating the tuning process. We evaluate our approach on SIMDEX, a job scheduling simulator, where it demonstrates superior performance over both the traditional requirements-oriented MPC framework CobRA and the nonadaptive ICNN-based controller, achieving a better balance of performance, resource efficiency, and control stability.
Large Language Models (LLMs) are transforming software engineering, including mobile Augmented Reality (AR) applications. AR software behavior often depends on dynamic environmental factors, making it difficult to use conventional testing and verification approaches. Metamorphic Testing (MT) offers an alternative by assessing whether expected transformations hold across varied conditions. However, there is limited work exploring how well LLMs can detect these transformations-Metamorphic Relations (MRs)-in applications. We propose a stability-driven evaluation framework that examines whether LLMs consistently apply MRs across rephrasings. Our study finds that StarCoder and CodeLlama exhibit higher stability in MR identification compared to the general-purpose model Gemma. Additionally, we use a multi-agent debate framework to investigate whether combining multiple perspectives improves consistency in MR identification. The debate mechanism reduces MR inconsistencies, leading to more stable MR identification across all MRs. While debate helps stabilize MR identification, our evaluation against humanlabeled ground truth reveals that stability alone does not always correlate with correctness. Some models maintain stable yet incorrect predictions(CodeLlama), whereas debate enhances both consistency and correctness alignment, making LLM reasoning more reliable. This work contributes a method to evaluate LLMs in the absence of ground truth, establishing stability as a metric for assessing model reliability. Applying a multi-agent debate framework offers a promising approach to enhancing LLM reliability, especially in contexts where the ground truth is elusive.
