TASK Quarterly

Design and decomposition of Microservices Architecture: A Systematic Literature Review

Vadim Peczyński — 2026-08-31

Context: The adoption of Microservice Architectures has grown significantly as a result of their ability to overcome key limitations of monolithic systems, such as limited scalability, maintenance difficulties, and technological lock-in. However, the design of microservice-based systems remains a complex and non-trivial task. This underscores the need for well-defined methodologies and techniques to support practitioners and software architects throughout the microservice design process.

Objective: The objective of this study is to examine effective microservices design practices, microservice identification techniques, the tools employed to support design activities, and the factors that drive the decomposition of systems into new microservices.

Methods: A Systematic Literature Review was conducted following the guidelines established by the PRISMA framework. From 609 studies that were retrieved from the search execution, 150 were selected and analyzed to answer the research questions.

Results: This SLR contributes by examining design tools used for decomposition, analyzing microservice decomposition processes, identifying metrics for microservice decomposition, and framing microservice decomposition as a multi-objective optimization problem.

Conclusions: This study highlights the complexity and variability inherent in microservices design, as well as the limited availability of tools to support this process. The findings identify microservices communication and service decomposition as key challenges, with the majority of existing research focusing primarily on the decomposition of monolithic systems.

Automatic extraction of the process of changes to legal acts

Julia Żęgota — 2026-08-31

In an era of frequent legislative changes and a growing volume of published laws, automating amendment-related processes is essential for effective analysis and interpretation of legal documents. Manually tracking and describing relationships between new and ammending acts in the Journal of Laws of the Republic of Poland is time-consuming and prone to error. These tasks can be supported through metadata extraction tools and process mining algorithms. This work presents a system for automatic extraction and visualization of amendment processes. Parsing tools were applied to obtain and process the texts of laws, transforming the extracted metadata into event logs and a Directly-Follows Graph (DFG) for process mining. A web-based application provides graphical representation of the data. The resulting tool automatically generates a graph illustrating relationships between acts and their evolution over time. A survey of legal professionals and non-expert users confirms improved efficiency and readability in identifying changes and visualizing legislative paths. The tool offers practical support for lawyers and analysts working with historical and current legislative processes.

Glaucoma Detection Using Intelligent Analysis of Fundus Images: AI Pipeline, Mobile Application, and Low-Cost Fundus Camera Prototype

Karolina Glaza — 2026-08-31

Glaucoma is the second leading cause of irreversible blindness worldwide, affecting over 80 million people, with projections reaching 111 million by 2040. Early detection is critical, yet up to 50% of patients remain undiagnosed. This paper presents an integrated system for automated glaucoma screening combining a deep learning image analysis pipeline, an iOS mobile application, and a low-cost fundus camera prototype. The AI pipeline follows a two-stage architecture: a YOLOv9 model for region-of-interest (ROI) detection, followed by a UNet++ segmentation model for optic disc and cup delineation. The Cup-to-Disc Ratio (CDR) is computed from the resulting masks and used to classify glaucoma risk. Five YOLO model variants (v8, v9, v11, v12, v26) were evaluated on an augmented dataset of over 6100 fundus images; YOLOv9 achieved the best overall balance with precision of 98%. UNet++ reached approximately 90% accuracy on the segmentation task. The iOS application, built in Swift with an MVVM architecture and a FastAPI backend hosted on Microsoft Azure, streams real-time analysis progress to prevent frozen-screen effects. The fundus camera prototype, constructed from a Volk 20D lens and a PVC tube mounted on an iPhone 14 Pro, enables retinal image capture at a cost below 1300 PLN. The system targets clinical screening workflows while remaining accessible to individual users.