ABSTRACT Microservice bad smells (MBSs) affect system quality. However, the lack of comprehensive and detailed explanations of MBSs and current classification methods does not comprehensively encompass the microservice characteristics, making it challenging to study and address MBSs. Existing studies have focused on MBS detection methods, but identifying and quantifying the effects of different smells and properly assessing system health remain challenging. This review aims to provide an exhaustive list of the MBSs and formulate a reasonable classification based on microservice characteristics. By reasonably assessing the harmfulness of smells, we would comprehensively explain different MBSs. We would explore microservice system evaluation methods and propose a microservice system health assessment model (MSHAM) based on MBSs. We conducted a systematic literature review (SLR) of the catalog and evaluation literature in the field of MBSs. We also combined the open questionnaire form to answer the research questions qualitatively and quantitatively. This paper presents a comprehensive list of 69 types of MBSs, incorporating a highly scalable classification. Following the quality characteristics in ISO/IEC 25010:2023, we determined the sets of characteristics affected by different smells, and provided detailed explanations of smells in the form of MBSs knowledge base. Furthermore, we introduced the MSHAM, revealing the quantification process of harmfulness. The formulated smells list and classification method capture MBS characteristics comprehensively, ensuring scalability. We have exposed the impact of MBSs, enhancing researcher and practitioner understanding. MSHAM provides a logical model to quantify MBSs harm and system health, supporting the development of real‐time health monitoring.

Knowledge graph (KG)-based service recommendation methods address issues such as data sparsity and cold start in real-world service recommendations by integrating external knowledge as auxiliary information. Recently, large language models (LLMs) have gained significant attention due to their powerful comprehension and reasoning capabilities. LLM-based recommendation systems also demonstrate advantages in interpretability and few-shot service reasoning. However, the integration of LLMs and KGs into existing service recommendation methods presents two major challenges: (1) the difficulty of aligning service recommendation tasks with language modeling tasks, and (2) the lack of interpretable quantification of the relationship between knowledge and personalized preferences. To address these challenges, this paper proposes WPKL (Weighted Preference Optimization based on KG and LLM). WPKL leverages external knowledge to assist LLMs in modeling user preferences and employs a hybrid graph neural network (GNN) framework to enhance preference representation. Additionally, a weighted preference optimization (WPO) approach is proposed to fine-tune the LLM, enabling interpretable quantification of user preferences and personalized knowledge. Extensive experimental results demonstrate that WPKL achieves high-quality service recommendations.

Chatbots have advanced from basic conversational agents to versatile tools by integrating external services. However, traditional chatbots are constrained by predefined service boundaries, limiting their ability to handle complex tasks with unintegrated services. While most research focuses on improving service discovery and invocation through data-intensive pretraining, only 13.29% of services are well-documented, hindering practical deployment. This paper proposes a self-improving workflow for chatbots, using a “wide in, strict out” self-supervised learning approach to acquire domain knowledge efficiently and generate high-quality service documents. Compatible with existing methods, it eliminates the need for dataset collection or pre-training. Experiments demonstrate that our workflow significantly improves the pass and success rate of chatbots in utilizing unintegrated services, offering a powerful solution for real-world applications where service integration is limited.

To address the limitations of existing microservice bad smell (MBS) detection and refactoring tools, particularly the lack of fully automated architectural bad smell refactoring solutions, this paper proposes a MAPE-K-based self-adaptive framework for microservice bad smell refactoring (SABER). The framework aims to eliminate architectural smells through closed-loop self-repair, thereby reducing risks related to main-tainability, scalability, and security. SABER employs a cloud-edge collaborative architecture: edge-side components collect real-time metrics from a Kubernetes cluster, while cloud-side components detect architectural smells and dynamically generate refactoring strategies. These strategies include service merging, splitting, adding, and adjustment. By automatically executing these strategies, SABER achieves architectural bad smell elimi-nation. Experimental results show that the framework achieves 95.53 % precision and 84.71 % recall across ten benchmark systems, significantly improving refactoring efficiency compared to semi-automated and manual methods. Its deep integration with DevOps pipelines validates its effectiveness in sustaining microservice health, offering a novel paradigm for autonomous maintenance in distributed systems.

In the era of industrial internet, achieving a dynamic balance between mass production and personalized customization has become a core demand for industrial development. This necessitates that product service systems can accurately capture users' personalized requirements. Although large language models (LLMs) possess powerful dialogue and reasoning capabilities, enabling them to identify implicit requirements, they still exhibit limitations in supply-demand matching, particularly in the precise alignment between personalized requirements and product capabilities. To this end, this study innovatively proposes a personalized product customization service framework (Req2Sol) based on fine-grained supply-demand cognition. This framework formalizes the modeling of supply-demand capabilities and finegrained personalized requirements through a knowledge graph (KG), integrating them into the LLM training process. This significantly enhances the model's understanding of supplydemand relationships, enabling accurate product configuration and customization recommendations. Firstly, a multi-view modeling approach for supply-demand capabilities and personalized fine-grained requirements is proposed, constructing a requirementproduct knowledge graph. Secondly, the knowledge graph is utilized as pre-training data to achieve domain-specific finetuning of LLMs. By introducing conditional scenarios and strategies, a five-level quantitative evaluation system for Req2Sol is established, improving its performance by 7.3 % compared to the baseline model when handling unconventional or inaccurately expressed user requirements. Finally, using the air conditioning domain as a case study, the effectiveness of the framework in achieving precise supply-demand cognition and customized product recommendations is validated through the Req2Sol-QAS system developed by invoking Req2Sol services.

The integration of Mobile Edge Computing (MEC) into the Industrial Internet of Things (IIoT) has markedly improved resource accessibility and propelled digital-intelligent advancements. Nevertheless, the substantial costs of MEC infrastructure also pose critical challenges to incentive mechanism design. Specifically, there is still an absence of a standardized and widely recognized incentive framework for the dynamic and non-cooperative interactions between edge service requesters and providers. Furthermore, the highly complicated characteristics of IIoT necessitate a greater reliance on dependable and trustworthy edge resource provision than other paradigms, which implies that human-centric factors (e.g., credit) are equally crucial as profit-driven metrics (e.g., price) in incentive design. To tackle these challenges, we propose In3Edge, a Stackelberg game-based incentive mechanism that systematically considers the interplay between profit-driven and interest-oriented indicators while accommodating heterogeneous peers, subjective interest divergences, and objective resource disparities. Particularly, leveraging convex optimization theory, we provide rigorous proofs and in-depth analyses of the intrinsic properties of In3Edge, encompassing the concavity/convexity of utility functions, equilibrium solution boundaries, optimal responses under peer/interest heterogeneity, and closed-form solutions for symmetric multipeer scenarios while articulating a series of propositions and theorems to underpin future research. Finally, extensive experiments are constructed under diverse dynamically changing scenarios with distinct characteristics, confirming the strong motivational capabilities of In3Edge in MEC-empowered IIoT.

ABSTRACT Microservice bad smells, arising from poor design and development practices, can severely degrade system quality if unaddressed. While rule‐based detection methods exist, their applicability is limited by subjective metric thresholds and the difficulty in defining certain bad smells, particularly complex microservice bad smells that are challenging to express through rules or involve high subjectivity. These smells often involve multiple services or manifest across multiple layers within a service, making them particularly challenging to detect using traditional methods. Without efficient and accurate detection mechanisms, the self‐healing capabilities of microservices during operation and continuous evolution will also be compromised. Given the promise of machine learning in code smell detection, this study empirically evaluates its performance in detecting complex microservice bad smells. We employ two sampling techniques and eight classification models on 1180 samples from 55 systems, generating 45 detection models and identifying top classifiers for seven complex microservice bad smell types. We compare machine learning with rule‐based methods for high‐subjectivity smells, analyze performance gaps, and propose a MAPE‐K‐based conceptual framework for runtime detection and refactoring. Finally, we discuss the necessity for future research.