Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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Correction Automating Software Size Measurement From Python Code Using Language Models (Vol 33, 19, 2026)(Springer, 2025) Tenekeci, Samet; Unlu, Huseyin; Gul, Bedir Arda; Keles, Damla; Kucuk, Murat; Demirors, OnurArticle Automating Software Size Measurement from Python Code Using Language Models(Springer, 2025) Tenekeci, Samet; Unlu, Huseyin; Gul, Bedir Arda; Keles, Damla; Kuuk, Murat; Demirors, OnurSoftware size is a key input for project planning, effort estimation, and productivity analysis. While pre-trained language models have shown promise in deriving functional size from natural-language requirements, measuring size directly from source code remains under-explored. Yet, code-based size measurement is critical in modern workflows where requirement documents are often incomplete or unavailable, especially in Agile development environments. This exploratory study investigates the use of CodeBERT, a pre-trained bimodal transformer model, for measuring software size directly from Python source code according to two measurement methods: COSMIC Function Points and MicroM. We construct two curated datasets from the Python subset of the CodeSearchNet corpus, and manually annotate each function with its corresponding size. Our experimental results show that CodeBERT can successfully measure COSMIC data movements with up to 91.4% accuracy and generalize to the functional, architectural, and algorithmic event types defined in MicroM, reaching up to 81.5% accuracy. These findings highlight the potential of code-based language models for automated functional size measurement when requirement artifacts are absent or unreliable.Conference Object Microarc: Event Driven Analysis and Design Method for Microservices(Elsevier B.V., 2025) Yıldız, Ali; Demirors, OnurThe rapid development of the Internet infrastructure has enabled software applications to leverage almost unlimited and scalable resources. Microservice-based architecture has emerged as a solution to harness the benefits of a distributed cloud-based infrastructure. Event-driven architecture is a powerful approach for addressing challenges in distributed systems, such as scalability, distributed data, and sharing of data at scale. In an event-driven microservice architecture, decoupled services interact by responding to events and event streams facilitate data sharing between them. Despite these advantages, there is no de facto method for the analysis and design of systems within microservice architecture. Organizations often face difficulties in developing microservice-based systems, owing to the lack of well-defined methodologies for analysis and design. In this study, we present an analysis and design method for microservice-based systems. MicroArc is a method for analyzing and designing microservice-based systems, and comprises modeling notations, guiding processes to articulate how the method is applied, and a supporting tool for modelling. The MicroArc approach enables the identification of events and microservice candidates by modeling the flow of processes in the early phase of development. © 2025 Elsevier B.V., All rights reserved.