Feb 2025· International Conference on Evaluation of Novel Approaches to Software Engineering· Vol abs/2502.09766· 16 citations· ⚡ 1 influential· 38 references
Computer Science
TL;DR
A system that uses Large Language Models (LLMs) to automate the API-first development of RESTful microservices and assists in creating OpenAPI specification, generating server code from it, and refining the code through a feedback loop that analyzes execution logs and error messages is presented.
Abstract
The growing need for scalable, maintainable, and fast-deploying systems has made microservice architecture widely popular in software development. This paper presents a system that uses Large Language Models (LLMs) to automate the API-first development of RESTful microservices. This system assists in creating OpenAPI specification, generating server code from it, and refining the code through a feedback loop that analyzes execution logs and error messages. By focusing on the API-first methodology, this system ensures that microservices are designed with well-defined interfaces, promoting consistency and reliability across the development life-cycle. The integration of log analysis enables the LLM to detect and address issues efficiently, reducing the number of iterations required to produce functional and robust services. This process automates the generation of microservices and also simplifies the debugging and refinement phases, allowing developers to focus on higher-level design and integration tasks. This system has the potential to benefit software developers, architects, and organizations to speed up software development cycles and reducing manual effort. To assess the potential of the system, we conducted surveys with six industry practitioners. After surveying practitioners, the system demonstrated notable advantages in enhancing development speed, automating repetitive tasks, and simplifying the prototyping process. While experienced developers appreciated its efficiency for specific tasks, some expressed concerns about its limitations in handling advanced customizations and larger scale projects. The code is publicly available at https://github.com/sirbh/code-gen
This research focuses on Maven configuration and structure updates and explores how the CodeT5 model can automate the migration of Java 8 projects to Java 17, finding the model accelerates parts of migration but remains unsuitable for fully automated use.
Ayush Luhar, Dev Trivedi, Vatsal Patel et al.· International Conference on...· 0 citations
Microservice-based systems evolve continuously through API changes, service splits and merges, dependency churn, and deployment-topology drift driven by DevOps practices. This rapid evolution erodes the assumptions encoded in formal models, test suites, and architectural descriptions, leaving practitioners with limited guidance on how to keep verification artifacts aligned with evolving implementations. Prior surveys cover microservices design, deployment, performance, and isolated verification techniques, but, to our knowledge, none consolidate the intersection of evolution and formal/rigorous verification, nor map how repository-derived signals and CI/CD pipelines support continuous verification of microservice-based systems. This paper addresses that gap through a systematic literature review (SLR). Following the Kitchenham et al. guidelines and PRISMA-style reporting, we defined a review protocol with four research questions covering (i) formal modeling and verification approaches, (ii) the impact of architectural and API evolution on verifiability, (iii) repository-derived signals for updating formal artifacts, and (iv) continuous and incremental verification in CI/CD pipelines. Structured searches in IEEE Xplore, ACM Digital Library, SpringerLink, and Scopus returned 1187 records, which were screened in seven stages and reduced to 18 included studies (13 primary studies that supply evidence and five background studies retained for transparency and framing) through inclusion/exclusion criteria and a weighted 15-item quality-assessment instrument. We synthesized the included studies thematically and compared them along five dimensions: verification technique, evolution dimension, automation level, empirical evidence, and tool/artifact availability. The synthesis shows that actor-based model checking, TLA+ control-plane verification, static code analysis, continuous certification, and runtime/self-adaptive testing form five complementary clusters, but empirical evaluation is dominated by small academic case studies, only a minority of approaches automate model updates from repository signals, and few are integrated end-to-end into CI/CD. We discuss methodological limitations of the body of work, including a lack of industrial-scale validation and shared datasets, contrast intrinsic (build-time, model-driven) and extrinsic (recovery-based) verification strategies, and outline a research agenda toward repository-aware, pipeline-integrated verification for evolving microservices. The present synthesis distils a set of immediate, evidence-based actions for practitioners together with a focused agenda of open research challenges.
R. Gómez, Ebeid Elsayed, Enrique R. Zarate et al.· Software· 0 citations
A Domain-Specific Language (DSL), named RI language, designed for the declarative description of microservices, along with a supporting tool, TSE (Toolbox Service Executor), implemented in Python and based on the RI grammar, enables the representation of structural and operational aspects of services in a technology-agnostic manner.
Cássio Ritse, Machado Dos, Santos Silva et al.· 0 citations
The paper is trying to deeply analyze the pros and cons of implementing GenAI into software development, analysing current applications used in software development life cycle (SDLC), drawing upon case studies and programmer experience, and examining effect on code quality, team working and project timeline.
P. Arun, Jagdale, Deepti Ameta et al.· Economic Sciences· 0 citations
This study investigates the process of managing output events in microservice systems where business logic is isolated from infrastructure dependences and events are transmitted asynchronously by external event transport mechanisms.
The task addressed relates to the lack of a holistic architectural method for isolating business logic from event transport mechanisms. As their number increases, the application code becomes more complicated, its testability deteriorates, and technical debt also increases.
This work reports a method developed for centralized management of output events, consisting of six components (Key, Info, RoutingRule, EventContract, EventDispatcher, EventManager). It provides complete isolation of business logic from event transport mechanisms through declarative routing rules and dependence inversion. This method supports conditional and multicast routing without the need to make changes to the application code. The method is defined through abstract interfaces and is independent of the programming language by default. Its effectiveness has been confirmed on six control projects with a 2 × 3 design: two implementations (baseline and proposed) at three levels of complexity.
Applying the method to a project with three event transport mechanisms and fifteen use cases provides an increase in the use case maintainability index by 4.9 points (5.5%). Halstead effort is reduced by 47%, technical debt by 14.7%, average cyclomatic complexity by 28%; features with excessive cyclomatic complexity are absent compared to the baseline implementation. The applicability hypothesis has been empirically confirmed: the method is appropriate for microservices with more than two event transport mechanisms or more than ten use cases that generate events
V. Lakhai, M. Seniv· Eastern-European Journal of...· 0 citations
The findings demonstrate that structural evaluations of LLM-generated decompositions should explicitly control for mapping coverage, as apparent differences between prompting strategies may otherwise reflect methodological bias rather than genuine architectural quality.
D. Silva, Renan Alves, Emanuel Dantas et al.· arXiv.org· 0 citations
The new ChartNet training dataset could improve the accuracy of vision-language models that help analyze business trends or interpret scientific figures.
