Infrared unmanned aerial vehicle (UAV) target images often suffer from motion blur degradation caused by rapid sensor movement, significantly reducing contrast between target and background. Generally, detection performance heavily depends on the discriminative feature representation between target and background. Existing methods typically treat deblurring as a preprocessing step focused on visual quality, while neglecting the enhancement of task-relevant features crucial for detection. Improving feature representation for detection under blur conditions remains challenging. In this paper, we propose a novel Joint Feature-Domain Deblurring and Detection end-to-end framework, dubbed JFD³. We design a dual-branch architecture with shared weights, where the clear branch guides the blurred branch to enhance discriminative feature representation. Specifically, we first introduce a lightweight feature restoration network, where features from the clear branch serve as feature-level supervision to guide the blurred branch, thereby enhancing its distinctive capability for detection. We then propose a frequency structure guidance module that refines the structure prior from the restoration network and integrates it into shallow detection layers to enrich target structural information. Finally, a feature consistency self-supervised loss is imposed between the dual-branch detection backbones, driving the blurred branch to approximate the feature representations of the clear one. We also construct a benchmark, named IRBlurUAV, containing 30,000 simulated and 4,118 real infrared UAV target images with diverse motion blur. Extensive experiments on IRBlurUAV demonstrate that JFD³ achieves superior detection performance while maintaining real-time efficiency.

• Plang: A language blending natural prompt with control flow for precise LLM guidance. • Meta-prompt programming: Enables LLM to self-modify prompt programs during execution. • 40.75%-89.55% conciseness gain: Outperforms methods like LangChain in prompt coding. • Open-source solution: Enables multi-agent collaboration & tool use with minimal code. The advent of instruction-following large language models (LLMs), exemplified by ChatGPT, has significantly enhanced the performance of generative autoregressive natural language models on general tasks, marking a crucial milestone toward artificial general intelligence. While research has shown that LLM performance critically depends on prompt effectiveness, existing prompt construction approaches - including prompt string templates, prompt programming frameworks, and prompt programming syntactic sugar - suffer from limitations in imprecise generation control and deviation from natural language syntax, thereby impeding prompt engineering advancement. To address these challenges, we introduce PromptLanguage (Plang), a string-first programming language designed specifically for LLM prompt engineering. Our key innovation lies in utilizing font styles as syntax keywords to seamlessly integrate natural language prompt text with control flow code, enabling precise intervention in the generation process while maintaining the natural language affinity of prompt programs. Notably, Plang pioneers the concept of meta-prompt programming. Extensive experimental results across prompt engineering cases and quantitative analyses demonstrate that Plang-written prompt programs offer superior read/writability, intervention precision and up to 89.55% efficiency improvements. The Plang implementation is available as open-source software at https://github.com/HJZ-XDU/plang .

Infrared unmanned aerial vehicle (UAV) images captured using thermal detectors are often affected by temperature-dependent low-frequency nonuniformity, which significantly reduces the contrast of the images. Detecting UAV targets under nonuniform conditions is crucial in UAV surveillance applications. Existing methods typically treat infrared nonuniformity correction (NUC) as a preprocessing step for detection, which leads to suboptimal performance. Balancing the two tasks while enhancing detection-beneficial information remains challenging. In this paper, we present a detection-friendly union framework, termed UniCD, that simultaneously addresses both infrared NUC and UAV target detection tasks in an end-to-end manner. We first model NUC as a small number of parameter estimation problem jointly driven by priors and data to generate detection-conducive images. Then, we incorporate a new auxiliary loss with target mask supervision into the backbone of the infrared UAV target detection network to strengthen target features while suppressing the background. To better balance correction and detection, we introduce a detection-guided self-supervised loss to reduce feature discrepancies between the two tasks, thereby enhancing detection robustness to varying nonuniformity levels. Additionally, we construct a new benchmark composed of 50,000 infrared images in various nonuniformity types, multi-scale UAV targets and rich backgrounds with target annotations, called IRBFD. Extensive experiments on IRBFD demonstrate that our UniCD is a robust union framework for NUC and UAV target detection while achieving real-time processing capabilities. Dataset can be available at https://github.com/IVPLaboratory/UniCD.

Microservice architecture is used by leading companies to develop their large-scale software systems. These systems comprise numerous nodes, diverse service types and instances, and substantial volumes of data. Current research usually requires a central node to collect massive data from the system to build an anomaly detection model, encountering two significant limitations: 1) Most research trains a model for the entire system, ignoring the unique characteristics of individual nodes. Additionally, processing vast system-wide data in a single node imposes significant resource demands. 2) Microservice systems change frequently, and the historical data distribution differs significantly from the real data distribution, resulting in concept drift. Thus, we proposes DistriAD, a distributed anomaly detection method specifically designed for large-scale microservice systems. DistriAD involves a lightweight anomaly detection model deployed on each distributed node for precise anomaly detection, thus enhancing its accuracy. Furthermore, DistriAD utilizes a federated learning framework and a continuous updating method incorporating human feedback to update model parameters and address concept drift. Experimental validation on public datasets, e.g., TrainTicket-based and GAIA, and a proprietary test system dataset demonstrate that DistriAD outperforms baseline methods, improving F1-score up to 39.2 %. We believe that this work can provide insights into distributed anomaly detection in large-scale microservice systems, thereby improving their performance.

Microservice resource management aims to ensure stable service instance loads and improve overall resource utilization through load balancing, elastic scaling, and container orchestration while meeting system service quality requirements.Existing research often focuses on localized solutions, addressing only single aspects of elastic scaling or load balancing without recognizing the systemic nature of microservice resource management.The complexity and dynamism of service dependencies make it challenging to quantify interactions between resource strategies and service loads.Additionally, microservice systems experience dynamic load variations influenced by user behavior, business activities, and external factors.The dynamic nature of load changes and the selection of load features significantly increase the difficulty of resource forecasting, further complicating microservice resource management.To address this problem, this paper proposes MDRM (MAPEbased Dynamic Resource Management), a dynamic optimization method that integrates load balancing and elastic scaling to overcome the limitations of isolated strategies.MDRM models system load based on business characteristics and service invocation relationships, accurately capturing dynamic variations.A composite model, combining parallel multi-layer CNNs and LSTMs, extracts spatiotemporal microservice features, enhancing resource forecasting accuracy.Additionally, MDRM formulates a comprehensive load balancing optimization function that synergizes with resource utilization and service response time objectives to generate optimal management strategies.Experimental results demonstrate that, compared to default resource management strategies in Docker Swarm and Kubernetes, MDRM significantly improves system throughput (approximately 1000 RPS) and reduces response time (approximately 30-40 ms), proving its effectiveness.

Code review is essential for maintenance of pull request-based software systems. Recommending suitable reviewers for code changes can enhance defect detection and knowledge dissemination. Despite extensive research, the inherent complexity of pull requests (PRs) and reviewer profiles continues to cause challenge for accurate matching them together. Furthermore, existing methods often amplify gender and racial/ethnic disparities due to the lack of attention to biases present in historical review records. To address these issues, we first collected a dataset from 4 large-scale open-source projects involving 50 -month revision history, reaching up to 30 attributes. This dataset includes gender and racial/ethnic information, which was inferred, validated, and incorporated to enable comprehensive data bias analysis in reviewer recommendation tasks. Additionally, we introduce a fairness-aware and accurate approach: CoReBM, which leverages the advanced semantic understanding capabilities of Large Language Models (LLMs) to comprehensively capture the nuanced textual context of both PRs and reviewers, utilizing the robust planning, collaborative, and decision-making abilities of multi-agent systems. CoReBM integrates diverse factors to improve recommendation performance while mitigating bias effects through the incorporation of candidates' gender and racial/ethnic attributes. We evaluate the effectiveness of our approach on this dataset, and the results demonstrate that CoReBM outperforms state-of-the-art methods in both accuracy and fairness in recommendation.

Modern enterprises are increasingly adopting microservice architectures to enhance system flexibility and scalability. However, in the face of ever-changing business requirements, the relationships between system components have become increasingly complex, resulting in significant challenges in maintaining system robustness. In recent years, multimodal data-driven approaches based on graph neural networks have emerged as a predominant solution for root cause localization in microservice systems. Our detailed analysis of architectural characteristics and existing research reveals two critical limitations. First, simple graph is insufficient to represent the one-to-many relationships inherent in microservice component interactions, such as deployment, subordinate, and dependency. Second, the current multimodal data-based method has difficulty in performing localization on faults occurring on hosts, services, and instances at the same time.To address these challenges, we propose HyperRCA, a novel multi-granular root cause analysis approach based on hypergraph neural networks. Our approach models system states during faults via a hypergraph with instances as graph nodes, explicitly capturing heterogeneous relationships through three innovative hyperedge designs: deployment hyperedges for infrastructure relationships, subordinate hyperedges for service hierarchies, and dependency hyperedges for inter-component interactions. We used hypergraph neural networks and multi-layer perceptrons to train a root cause localization model based on hyperedge features to achieve multi-granularity root cause localization. Experimental evaluations demonstrate significant performance improvements over state-of-the-art approaches. HyperRCA achieves a maximum HR@5 improvement of 112.62% on single-granularity datasets and 466.43% in multi-granularity scenarios.

Assigning pull requests to appropriate code reviewers can accelerate the review process and help uncover potential bugs. However, the inherent complexities in pull requests and code reviewers present challenges in making suitable matches between them. Prior studies focus on mining rich semantic information from pull requests or profile information from code reviewers to improve efficiency. These approaches often overlook the intrinsic relationships between pull requests and code reviewers, which can be represented by a combination of multiple factors and strategies, resulting in suboptimal recommendation accuracy.

Underwater Image Enhancement (UIE) encounters substantial challenges due to the intricate nature of physical degradation processes that diminish visibility in underwater images. Existing methods leverage learning-based models to delineate pixel mappings for either paired or unpaired images to ameliorate the quality of degraded visuals. Nonetheless, the paucity of paired image datasets and the erratic nature of unsupervised learning methods considerably hinder advancements in UIE. This study presents an innovative contrastive learning framework specifically designed for UIE, aimed at effectively addressing the aforementioned challenges. Our strategy reconceptualizes image enhancement as a multi-task joint learning problem, thus fortifying the enhancement process. We pinpoint three pivotal aspects for UIE: contrastive feature learning, semantic information coherence, and cross-domain feature transfer. These elements are imperative for augmenting contrast, preserving texture integrity, and ensuring color accuracy. The contrastive learning paradigm empowers the enhancement module to discern between unpaired positive (high-quality) and negative (degraded) underwater images, facilitating semantic learning in refining the enhancement network systematically. By leveraging the semantic feature domain extracted from unpaired high-quality images, our method demonstrates superior performance, validated by several quality metrics, outperforming recent advancements in unsupervised UIE techniques.

Extract Method is one of the most frequently performed refactoring operations for the decomposition of large and complex methods, which can also be combined with other refactoring operations to remove a variety of design flaws. Several Extract Method refactoring tools have been proposed based on the quantification of extraction criteria. To the best of our knowledge, state-of-the-art related techniques can be broadly divided into two categories: the first line is non-machine-learning-based approaches built on heuristics, and the second line is machine learning-based approaches built on historical data. Most of these approaches characterize the extraction criteria by deriving software metrics from fine-grained code properties. However, in most cases, these metrics can be challenging to concretize, and their selections and thresholds also largely rely on expert knowledge. Thus, in this paper, we propose an approach to automatically recommend Extract Method refactoring opportunities named REMS via mining multi-view representations from code property graph. We fuse various representations together using compact bilinear pooling and further train machine learning classifiers to guide the extraction of suitable lines of code as new method. We evaluate our approach on two publicly available datasets. The results show that our approach outperforms five state-of-the-art refactoring tools including GEMS, JExtract, SEMI, JDeodorant, and Segmentation in effectiveness and usefulness. Our approach demonstrates an increase of 29% in precision, 15% in recall, and 23% in f1-measure. The results also unveil practical suggestions and provide new insights that benefit additional extract-related refactoring techniques.

Multi-scale infrared unmanned aerial vehicle (UAV) targets (IRUTs) detection under dynamic scenarios remains a challenging task due to weak target features, varying shapes and poses, and complex background interference. Current detection methods find it difficult to address the above issues accurately and efficiently. In this paper, we design a dynamic attentive network (DANet) incorporating a scale-adaptive feature enhancement mechanism (SaFEM) and an attention-guided cross-weighting feature aggregator (ACFA). The SaFEM adaptively adjusts the network's receptive fields at hierarchical network levels leveraging separable deformable convolution (SDC), which enhances the network's multi-scale IRUT awareness. The ACFA, modulated by two crossing attention mechanisms, strengthens structural and semantic properties on neighboring levels for the accurate representation of multi-scale IRUT features from different levels. A plug-and-play anti-distractor contrastive regularization (ADCR) is also imposed on our DANet, which enforces similarity on features of targets and distractors from a new uncompressed feature projector (UFP) to increase the network's anti-distractor ability in complex backgrounds. To further increase the multi-scale UAV detection performance of DANet while maintaining its efficiency superiority, we propose a novel scale-specific knowledge distiller (SSKD) based on a divide-and-conquer strategy. For the "divide'' stage, we intendedly construct three task-oriented teachers to learn tailored knowledge for small-, medium-, and large-scale IRUTs. For the "conquer'' stage, we propose a novel element-wise attentive distillation module (EADM), where we employ a pixel-wise attention mechanism to highlight teacher and student IRUT features, and incorporate IRUT-associated prior knowledge for the collaborative transfer of refined multi-scale IRUT features to our DANet. Extensive experiments on real infrared UAV datasets demonstrate that our DANet is able to detect multi-scale UAVs with a satisfactory balance between accuracy and efficiency.

The rise of containerization has led to the development of container cloud technology, which offers container deployment and management services. However, scheduling a large number of containers efficiently remains a significant challenge for container cloud service platforms. Traditional load prediction methods and scheduling algorithms do not fully consider interdependencies between containers or fine‐grained resource scheduling, leading to poor resource utilization and scheduling efficiency. To address these challenges, this paper proposes a new load prediction model CNN‐BiGRU‐Attention and a container scheduling strategy based on load prediction. The prediction model CNN and BiGRU focus on the local features of load data and long sequence dependencies, respectively, as well as introduce the attention mechanism to make the model more easily capture the features of long distance dependencies in the sequence. A container scheduling strategy based on load prediction is also designed, which first uses the load prediction model to predict the load state and then generates a scheduling strategy based on the load prediction value to determine the change of the number of container replicas in a fine‐grained manner based on the load prediction value in the next time window, while the established domain‐based container selection method is employed to facilitate the coarse‐grained online migration of containers. Experiments conducted using public datasets and open‐source simulation platforms demonstrate that the proposed approach achieves a 37.4% improvement in container load prediction accuracy and a 21.7% improvement in container scheduling efficiency compared to traditional methods. These results highlight the effectiveness of the proposed approach in addressing the challenges faced by container cloud service platforms.

Abstract Due to its development agility, continuous delivery, scalability and other characteristics, the microservice architecture systems (MASs) have provided complex business functions to hundreds of millions of users in many application fields. The operation and maintenance governance for a large number of microservices with complex relationships is crucial to ensuring the stability and reliability of an MAS. Although this research field has received certain attention and produced some innovative results, there is a lack of systematic reviews covering the different aspects of it. In this context, the central objective of this study is to carry out a systematic literature review (SLR) in this field, in an attempt to review existing issues, discuss the main trends, and share the findings with the academia. As a result, we start from more than 500 scientific papers published from 2009 to 2021 and extract 144 most significant papers, identify that the main research directions of this field include load balancing, fault detection, and autoscaling. Subsequently, we provide a comprehensive description of these research directions, discuss them in particular detail. We also determine limitations of current work and discuss new directions worth exploring in the future. Consequently, the outcomes will assist professionals and experts in the industry as well as academic researchers to focus more on operation and maintenance governance of MASs and further improve the relevant methods and theoretical systems in this field.

Owing to the high complexity of chip architecture and assay protocol, considerable effort has been directed toward the design automation of continuous-flow microfluidics over the past decade. Existing methods, however, perform the corresponding design tasks, including binding, scheduling, placement, and routing separately, leading to serious gaps between different steps and potentially even cause design failure. To overcome these drawbacks, in this article, we propose a one-pass design paradigm for continuous-flow microfluidic lab-on-a-chip systems, integrating all the design steps into an “organic whole,” which has never been considered in prior work. With the proposed paradigm, all the design tasks can be synchronized seamlessly and performed in a combined manner, thereby eliminating the gaps between design steps. Consequently, optimized biochip architectures can be generated without any design adjustments and modifications. The experimental results demonstrate the effectiveness of the proposed automation flows.

Background: Many safety-critical industrial applications have turned to deep learning systems as a fundamental component. Most of these systems rely on deep learning libraries, and bugs of such libraries can have irreparable consequences. Aims: Over the years, dependency structure has shown to be a practical indicator of software quality, widely used in numerous bug prediction techniques. The problem is that when analyzing bugs in deep learning libraries, researchers are unclear whether dependency structures still have a high correlation and which forms of dependency structures perform the best. Method: In this paper, we present a systematic investigation of the above question and implement a dependency structure-centric bug analysis tool: Depend4BL, capturing the interaction between dependency structures and bug locations in deep learning libraries. Results: We employ Depend4BL to analyze the top 5 open-source deep learning libraries on Github in terms of stars and forks, with 279,788 revision commits and 8,715 bug fixes. The results demonstrate the significant differences among syntactic, history, and semantic structures, and their vastly different impacts on bug locations. Their combinations have the potential to further improve bug prediction for deep learning libraries. Conclusions: In summary, our work provides a new perspective regarding to the correlation between dependency structures and bug locations in deep learning libraries. We release a large set of benchmarks and a prototype toolkit to automatically detect various forms of dependency structures for deep learning libraries. Our study also unveils useful findings based on quantitative and qualitative analysis that benefit bug prediction techniques for deep learning libraries.

Incorrect placement of methods within classes is a typical code smell called Feature Envy, which causes additional maintenance and cost during evolution. To remove this design flaw, several Move Method refactoring tools have been proposed. To the best of our knowledge, state-of-the-art related techniques can be broadly divided into two categories: the first line is non-machine-learning-based approaches built on software measurement, while the selection and thresholds of software metrics heavily rely on expert knowledge. The second line is machine learning-based approaches, which suggest Move Method refactoring by learning to extract features from code information. However, most approaches in this line treat different forms of code information identically, disregarding their significant variation on data analysis. In this paper, we propose an approach to recommend Move Method refactoring named RMove by automatically learning structural and semantic representation from code fragment respectively. We concatenate these representations together and further train the machine learning classifiers to guide the movement of method to suitable classes. We evaluate our approach on two publicly available datasets. The results show that our approach outperforms three state-of-the-art refactoring tools including PathMove, JDeodorant, and JMove in effectiveness and usefulness. The results also unveil useful findings and provide new insights that benefit other types of feature envy refactoring techniques.

Recent advances in continuous-flow microfluidics have enabled highly integrated lab-on-a-chip biochips. These chips can execute complex biochemical applications precisely and efficiently within a tiny area, but they require a large number of control ports and the corresponding control logic to generate required pressure patterns for flow control, which, consequently, offset their advantages and prevent their wide adoption. In this article, we propose the first flow-control layer co-synthesis flow called MiniControl, for continuous-flow microfluidic biochips under strict constraints for control ports, incorporating high-level synthesis, physical design, and control system design simultaneously, which has never been considered in previous work. With the maximum number of allowed control ports specified in advance, this synthesis flow aims to generate biochip architectures with high execution efficiency and the corresponding control systems with optimized timing performance. Besides, the overall cost of a biochip can be reduced and the tradeoff between a control system and execution efficiency of biochemical applications can be evaluated for the first time. The experimental results demonstrate that MiniControl leads to high execution efficiency, low platform cost, as well as excellent timing performance, while strictly satisfying the given control-port constraints.

Logs that record system abnormal states (anomaly logs) can be regarded as outliers, and the k-Nearest Neighbor (kNN) algorithm has relatively high accuracy in outlier detection methods. Therefore, we use the kNN algorithm to detect anomalies in the log data. However, there are some problems when using the kNN algorithm to detect anomalies, three of which are: excessive vector dimension leads to inefficient kNN algorithm, unlabeled log data cannot support the kNN algorithm, and the imbalance of the number of log data distorts the classification decision of kNN algorithm. In order to solve these three problems, we propose an efficient log anomaly detection method based on an improved kNN algorithm with an automatically labeled sample set. This method first proposes a log parsing method based on N-gram and frequent pattern mining (FPM) method, which reduces the dimension of the log vector converted with Term frequency.Inverse Document Frequency (TF-IDF) technology. Then we use clustering and self-training method to get labeled log data sample set from historical logs automatically. Finally, we improve the kNN algorithm using average weighting technology, which improves the accuracy of the kNN algorithm on unbalanced samples. The method in this article is validated on six log datasets with different types.

Self-Adaptive Systems (SASs) can dynamically adjust themselves to adapt to the changes, by planning some strategies to guide the adjustments. However, many uncertainties in runtime affect the efficiency of strategies and the ability to attain goals for SASs. So, it is necessary to validate strategies before they are executed. At present, most of the validation methods ignore the fact that less data can be observed at runtime, so it is difficult to describe the state accurately with these data. Most methods do not support uncertain state reasoning, but state transition is uncertain. This will result in the verification method not dealing well with the uncertainties. This paper proposes a strategy validation method based on Partially Observable Markov Decision Process, and makes several key contributions: (1) a uncertain state model that not only support description of states through partial information, but also describe the uncertain transitions of states, (2) a validation method to validate whether the strategies meets the requirements at runtime, (3) a strategy correction method to get effective strategies as quickly as possible.

Self-Adaptive Systems (SASs) need to analyze software changes accurately and continuously, that is, recognize events caused by changes, and adjust structure or behavior.However, present event recognition methods frequently monitor events, resulting in waste of system resources.And most of them ignore the impact of operating environment uncertainty, causing errors in recognizing the event and directly affecting the reliability of SASs.Addressing the above problems, this paper proposes an event recognition method based on "detection-recognition" mechanism.Firstly, the Naive Bayesian Classification algorithm is used to detect the state of the system.If the system is judged to be abnormal, we will combine with rule reasoning and fuzzy reasoning to recognize events.The system does not have to monitor the occurrence of events from time to time, avoiding the waste of system resources.Moreover, the probabilistic reasoning method of Bayesian Classification and the introduction of fuzzy reasoning can cope with environmental uncertainty and improve the accuracy of event recognition.Finally, we exemplify this mechanism with the Web system, which proves the effectiveness of the methods.

The changes confronting contemporary Self-Adaptive Systems (SASs) are characterized by uncertainties in their relationships, priorities, and contexts. To generate adaptation strategies for handling these changes, existing adaptation planning methods, which ignore these uncertainties, must be improved. This thesis explores the possibilities of using Search-Based Software Engineering (SBSE) to establish a search-based planning method capable of handling multiple changes in an uncertain context without defining their priorities. Meanwhile, both the assurance approach to improving the efficiency of adaptation planning and the selection approach to choosing a unique strategy are proposed to solve emerging research questions that arise when such planning method is applied in actual SASs. From this experience, we are able to derive innovative methods for the designers of SASs as a reference, which may observably improve the ability of SASs and promote the widespread use of SBSE in SASs.

Future-generation SASs need to have the adaptive abilities to efficiently handle changes from different sources and to mitigate conflicts caused by multiple simultaneous changes. However, existing methods cannot simultaneously make Future-generation SASs have the above abilities. This paper proposes an adaptive system framework based on agent technology and search-based software engineering technology (SBSE) for developing future-generation SASs with above-mentioned abilities. The framework integrates a hybrid adaptation logic based on agents to deal with various software changes from different layers, and an adaptation planning method with search-based optimization mechanism to mitigate conflicts caused by multiple simultaneous changes.

Planning a suitable solution to adapt to software changes is the most important and fundamental ability of self-adaptive software (SAS). However, with the increasing complexities of managed resources, context and user preferences, existing self-adaptive planning approaches need to be improved to deal with the complex changes which are multiple, interrelated and evolving. Search-based optimization (SBO) is well-suited to deal with multiple and complex problems. Hence, using SBO as a new self-adaptive planning approach may be a particularly promising research trajectory. This paper proposes a multi-agent framework for SAS with SBO to deal with complex changes, reduce maintenance time and cost, and enhance software quality. This framework defines a special software architecture of SAS to choose different planning approaches, uses the SBO to plan solutions for complex changes, and supports the online planning by multi agents. In addition, a corresponding workbench is being established to develop SAS according to this framework.

Some methods and auxiliary platforms/tools have been proposed to design and develop Self-Adaptive Systems (SASs).However, these methods and tools ignore how to turn existing systems to SASs.Therefore, the paper proposes a multiagentbased toolkit for adding self-adaptive abilities to the existing systems.With the Agent Packager and Rule Designer in it, existing systems are transformed into Multi-Agent Systems (MAS), without modifying codes.Based on the Supporting Platform in MABT, MAS run normally and achieves self-adaption with both Global and Local approach.