Post-release user feedback is a major control signal for maintenance and evolution in modern software development, yet it is noisy, fragmented, and difficult to translate into developer-usable issue specifications. Large Language Models (LLMs) can assist this transformation, but they often hallucinate or over-commit when evidence is weak, conflicting, or incomplete, limiting their robustness in automated software engineering workflows. We propose AGR (Agent-Governed Retrieval-Augmented Generation), a framework that regulates evidence acquisition and generation decisions via agentic control. AGR first applies an agentic triage step to filter low-signal or off-topic feedback, then retrieves evidence from a three-category hierarchy comprising official documentation, historical bug reports, and targeted web sources. It further performs confidence-weighted fusion across authoritative categories and uses an agentic decision module to verify relevance and sufficiency, trigger additional retrieval or online search when needed, reuse prior reports via memory, and abstain when evidence-supported grounding cannot be established. We evaluate AGR on two open-source software ecosystems, Firefox and VS Code. Results show that AGR achieves strong decision accuracy in triage and evidence verification, and produces more actionable and engineering-useful issue specifications than both raw feedback and a strong LLM baseline, while reducing unsupported details.

In self-adaptive software systems, the role of context is paramount, especially for proactive self-adaptation. Current research, however, does not fully explore context's impact, for example on priorities of the requirements. To address this gap, we introduce a novel contextual goal model to capture these factors and their influence on the system. Using this, we propose a two-layer control mechanism with a context-aware model predictive control to achieve proactive adaptation for the software system and adaptation for the controller itself. By contextual prediction and a more accurate system model, our approach utilizes model predictive control to facilitate timely and efficient system adaptations, improving both performance and adaptability. Meanwhile, we perform requirement adaptation to update the contextual goal model, which in turn updates the objective function and constraints of the controller. Our experimental evaluations across two scenarios demonstrate the significant benefits of our approach in enhancing system performance.

Attention Deficit Hyperactivity Disorder (ADHD) is a common neurodevelopmental condition that can affect individuals across lifespan and is characterized by inattention, hyperactivity, and impulsivity. Traditional ADHD diagnostic methods commonly rely on subjective assessments, leading to potential inaccuracies. This study proposes a novel mobile game that utilizes a combined continuous screening paradigm (CCSP), integrating classical psychological measures such as the Continuous Performance, Simon, and Go/No-Go tasks to provide a 4-minute ADHD screening tool for children. The serious game screening design focuses on the two dimensions of sustained attention, and hyperactivity-impulsivity in ADHD and was verified using a test comparing children with ADHD and neurotypical children. The screening model achieved a sensitivity of 0.82. This study provides a methodological framework for leveraging serious games on mobile platforms to facilitate early ADHD screening.

Despite its success in many applications, federated learning is increasingly vulnerable to sophisticated poisoning attacks. Existing defenses, particularly Byzantine Robust Aggregation Rules (BRARs), offer some protection but rely on strong assumptions or challenging technical prerequisites. To address these shortcomings, we propose anensemble defense with provably convergent aggregation(EndPCA). By using the entropy weight method to consolidate scores from multiple BRARs into an ensemble trust score, it effectively integrates heterogeneous weak BRARs to resist a wide range of poisoning attacks under practical assumptions. We formally prove that EndPCA can provide theoretical guarantees of convergence with bounded error. Our empirical evaluations show that EndPCA consistently outperforms existing BRARs, demonstrating its effectiveness across various scenarios.

Abstract Federated Learning (FL) enables multiple clients to collaboratively train a shared model without exposing local data, making it a fundamental paradigm for large-scale distributed intelligence. However, in practical edge-cloud deployments, the server must inspect a large volume of high-dimensional client updates within tight communication windows, which makes secure aggregation a problem closely tied to parallel processing, real-time response, and high-performance computing (HPC) resources. Among the major threats to FL, backdoor attacks are particularly insidious because they implant malicious behaviors into the global model while preserving benign-task performance. Although pre-aggregation defenses based on gradient analysis are promising, the current state-of-the-art methods such as Scope suffer from two key limitations: fixed parameters are ineffective against adaptive attackers, and single-point clustering is vulnerable to failure under heterogeneous (non-IID) data distributions. To address these limitations, we propose FAROS, a robust and HPC-friendly defense framework that generalizes the transform-and-cluster paradigm. FAROS incorporates two key components: Adaptive Differential Scaling (ADS), which dynamically adjusts defense sensitivity according to the dispersion of client gradients, and Robust Core-set Computing (RCC), which replaces single-point clustering with a consensus-based centroid derived from a stable core-set. This design improves robustness while preserving server-side efficiency through vectorizable similarity computation and parallelizable filtering. Extensive experiments on multiple datasets, models, and attack settings show that FAROS consistently outperforms existing defenses in both attack suppression and benign-task accuracy, while remaining compatible with scalable distributed FL infrastructures.

Multi-agent path finding (MAPF) is a safety-critical scenario where the goal is to secure collision-free trajectories from initial to desired locations. However, due to system complexity and uncertainty, integrating learning-based controllers with MAPF is challenging and cannot theoretically guarantee the safety of the learned controllers. In response, our study proposes a verified safe multi-agent neural control (VSMANC) approach for MAPF, focusing on the unified training of Decentralized Control Barrier Functions (DCBF) and controllers to enhence safety. VSMANC enables all agents to concurrently learn controllers and DCBFs using a unified loss function designed to maximize safety, adhere to standard control policies, and incorporate path-finding-related heuristics. We also propose a formal verification-guided retraining process to both verify the properties of the learned DCBFs and generate counterexamples for retraining, thereby providing a verified safety guarantee. We validate our approach through shape formation experiments and UAV simulations, demonstrating significant improvements in safety and effectiveness in complex multi-agent environments.

Social media platforms such as Twitter, Reddit, and Sina Weibo playa crucial role in global communication but often encounter strict regulations in geopolitically sensitive regions. This situation has prompted users to ingeniously modify their way of communicating, frequently resorting to coded language in these regulated social media environments. This shift in communication is not merely a strategy to counteract regulation, but a vivid manifestation of language evolution, demonstrating how language naturally evolves under societal and technological pressures. Studying the evolution of language in regulated social media contexts is of significant importance for ensuring freedom of speech, optimizing content moderation, and advancing linguistic research. This paper proposes a multi-agent simulation frame-work using Large Language Models (LLMs) to explore the evolution of user language in regulated social media environments. The framework employs LLM-driven agents: supervisory agent who enforce dialogue supervision and participant agents who evolve their language strategies while engaging in conversation, simulating the evolution of communication styles under strict regulations aimed at evading social media regulation. The study evaluates the framework's effectiveness through a range of scenarios from abstract scenarios to real-world situations. Key findings indicate that LLMs are capable of simulating nuanced language dynamics and interactions in constrained settings, showing improvement in both evading supervision and information accuracy as evolution progresses. Furthermore, it was found that LLM agents adopt different strategies for different scenarios. The reproduction kit can be accessed at https://github.com/BlueLinkXlGA-MAS.

Correct behavior of flight software against its requirements is a prime concern spanning its design, implementation, and operation. The emergence of “New Space” presents new challenges associated with small-scale missions which often involve open software frameworks, are developed by diverse teams, and employ rapid development methodologies which may not enjoy the rigorous quality assurance that institutional missions do. Hence, there is an overarching need to incor-porate contemporary engineering techniques and methods for checking requirement satisfaction for such flight software. To this end, this paper proposes GOP RIM E, designed to integrate goal monitoring within F’, a renowned software development framework developed by the Jet Propulsion Laboratory for embedded and spaceflight systems. GOPRIME consists of three phases: (i) a design phase, where annotations are used to align system-level objectives with architectural components; (ii) an implementation phase, where code generation tailored for the DSL of F’ is used to seamlessly automate the integration of goal monitoring functionality, and (iii) the operational phase, where the runtime state of annotated components is monitored, enabling evaluation of satisfaction of the overall goal model. We assess the development and operation overheads of instrumenting runtime goal monitoring over a characteristic case of a miniaturized satellite application.

Advancements in large language models (LLMs) are transforming software engineering through innovative prompt engineering strategies. By analyzing prompt-driven enhancements across key software engineering tasks, we present a sys-tematic literature review and a pioneering taxonomy elucidating the practical applications of prompt engineering in software engineering. Our taxonomy offers a foundational framework that clarifies the roles of prompt engineering and measures its impact, thereby guiding evolving AI -driven software engineering research and practices.

Security attacks present unique challenges to the design of self-adaptation mechanism for software-intensive systems due to the adversarial nature of the environment. Game-theoretical approaches have been explored in security to model malicious behaviors and design reliable defense for the system in a mathematically grounded manner. However, modeling the system as a single player, as done in prior works, is insufficient for the system under partial compromise and for the design of fine-grained defensive policies where the rest of the system with autonomy can cooperate to mitigate the impact of attacks. To address such issues, we propose a new self-adaptation framework incorporating Bayesian game theory and model the defender (i.e., the system) at the granularity of components. Under security attacks, the architecture model of the system is automatically translated, by the proposed translation process with designed algorithms, into a multi-player Bayesian game. This representation allows each component to be modeled as an independent player, while security attacks are encoded as variant types for the components. By solving for pure equilibrium (i.e., adaptation response), the system’s optimal defensive strategy is dynamically computed, enhancing system resilience against security attacks by maximizing system utility. We validate the effectiveness of our framework through two sets of experiments using generic benchmark tasks tailored for the security domain. Additionally, we exemplify the practical application of our approach through a real-world implementation in the Secure Water Treatment System to demonstrate the applicability and potency in mitigating security risks.

Self-adaptive systems (SASs) are designed to handle changes and uncertainties through a feedback loop with four core functionalities: monitoring, analyzing, planning, and execution. Recently, generative artificial intelligence (GenAI), especially the area of large language models, has shown impressive performance in data comprehension and logical reasoning. These capabilities are highly aligned with the functionalities required in SASs, suggesting a strong potential to employ GenAI to enhance SASs. However, the specific benefits and challenges of employing GenAI in SASs remain unclear. Yet, providing a comprehensive understanding of these benefits and challenges is complex due to several reasons: limited publications in the SAS field, the technological and application diversity within SASs, and the rapid evolution of GenAI technologies. To that end, this article aims to provide researchers and practitioners a comprehensive snapshot that outlines the potential benefits and challenges of employing GenAI’s within SAS. Specifically, we gather, filter, and analyze literature from four distinct research fields and organize them into two main categories to potential benefits: (i) enhancements to the autonomy of SASs centered around the specific functions of the MAPE-K feedback loop, and (ii) improvements in the interaction between humans and SASs within human-on-the-loop settings. From our study, we outline a research roadmap that highlights the challenges of integrating GenAI into SASs. The roadmap starts with outlining key research challenges that need to be tackled to exploit the potential for applying GenAI in the field of SAS. The roadmap concludes with a practical reflection, elaborating on current shortcomings of GenAI and proposing possible mitigation strategies. †

Decision making in self-adaptive systems often involves trade-offs between multiple quality attributes, with user preferences that indicate the relative importance and priorities among the attributes. However, eliciting such preferences accurately from users is a difficult task, as they may find it challenging to specify their preference in a precise, mathematical form. Instead, they may have an easier time expressing their displeasure when the system does not exhibit behaviors that satisfy their internal preferences. Furthermore, the user’s preference may change over time depending on the environmental context; thus, the system may be required to continuously adapt its behavior to satisfy this change in preference. However, existing self-adaptive frameworks do not explicitly consider dynamic human preference as one of the sources of uncertainty. In this paper, we propose a new adaptation framework that is specifically designed to support self-adaptation to user preference. Our framework takes a human-on-the-loop approach where the user is given an ability to intervene and indicate dissatisfaction and corrections with the current behavior of the system; in such a scenario, the system automatically updates the existing preference values so that the new, resulting behavior of the system is consistent with the user’s notion of satisfactory behavior. To perform this adaptation, we propose a novel similarity analysis to produce changes in the preference that are optimal with respect to the system utility. We illustrate our approach in a case study involving a delivery robot system. Our preliminary results indicate that our approach can effectively adapt its behavior to changing human preference.

3D object detection plays a vital role in the perception system of self-driving cars for it provides accurate structural information and classification of objects in the scene. Recent works leverage pseudo point clouds to compensate for the sparsity of raw point clouds. However, the coarse pseudo point cloud generation brings huge computational costs to the inference process, resulting in inferior inference speed. In this work, we aim to solve two critical yet not well-addressed issues in pseudo point cloud generation, including the loss of 3D detection inference speed and the additional memory cost due to the massive generation of the pseudo point cloud. We propose attention-guided pseudo point cloud generation to direct the focus of pseudo point cloud generation to hazardous regions. In our experiments, our method improves the inference speed by 21.72% and reduces the memory usage of generated data by 90.03%.

Reinforcement learning is a powerful methodology that enables self-adaptive systems to relearn and update their adaptation policy when dealing with unforeseen changes. To update the policy more efficiently, several knowledge reuse approaches have been proposed to speed up relearning. However, the current studies treat and reuse the knowledge integrally, which may result in increased relearning costs if the reused knowledge is inappropriate in the changed situation. Generally, some localized pieces of the knowledge are still appropriate for reuse if they are not related to the changes, while some pieces may become inappropriate for reuse if they are affected by the changes. This paper proposes a goal-oriented curriculum evolution method to realize finer-grained knowledge reuse, combining goal-oriented modeling and curriculum learning. The method is twofold: (1) at design time, we apply goal-oriented modeling to design a curriculum in which an RL problem is decomposed into sub-problems, so that knowledge can be decomposed into several pieces of localized knowledge for sub-problems, and (2) at runtime, we evolve the curriculum to reflect changes (i.e., update the sub-problems related to the changes), so that the affected pieces of knowledge can be locally updated to make them appropriate for reuse in the changed situation. The evaluation based on a cleaning robot shows that the relearning time was shortened, demonstrating the effectiveness of our method.

In the studies of self-adaptive systems (SAS), requirement relaxation is a widely discussed approach for managing the system’s requirements when dealing with the runtime environment changes (e.g., ignoring low-priority requirements to guarantee high-priority requirements). Guaranteeable requirement analysis (GRA) is recently proposed to determine the relaxation by checking the feasibility of all requirement combinations, enabling the SAS to realize the relaxation autonomously. However, a critical problem of GRA is the trade-off between analysis/relaxation precision and computation time at different granularity levels of requirements. Specifically, the analysis may not be precise enough if the requirements are coarse-grained (i.e., high granularity level), while the analysis may take a too long time if the requirements are fine-grained (i.e., low granularity level). This paper proposed a method, namely iterative adaptation via multi-grained requirement relaxation, to achieve the advantages of high precision and short computation time. Specifically, the SAS first deploys a rapid (but imprecise) relaxation using high granularity-level requirements. It then repeatedly iterates to a preciser (but slower) relaxation with a progressive decrease in the granularity level. An experiment based on the warehouse robot system demonstrates the validity of our proposal.

In event-based systems, safety properties are critical requirements to prevent the system from bad things happen. However, safety properties may be violated because of the runtime system functional fault. From the viewpoint of a self-adaptive system, such a system should be requirement-aware and changes its behavior to satisfy the designed requirements as much as possible. The previous work proposed a method to analyze possible adaptation options with degrading different requirements. Here, we propose an efficient difference analysis algorithm to shorten the analysis time so that the adaptation to functional fault can be more timely. Our idea is to reuse the analysis result of development time and re-analyze the changed part only, instead of performing the complete analysis from scratch. We evaluated our algorithm's efficiency based on three case studies: a coalmine pump-control system, a cyber-physical security people-flow restriction system, and a factory production cell system. The experiment results indicate that our algorithm averagely reduces 75.9% of analysis time compared with the existing analysis technique.