CC4M: Code Clone Analysis and Visualization for Microservices
Gen Kawamata, Yuki Ota, Norihiro Yoshida, Shiyu Yang, Erina Makihara, Katsuro Inoue
Abstract
Microservice architecture supports software evolution by decomposing a system into small, loosely coupled services that can be deployed independently. Contrary to the expectation of high modularity, prior studies have reported that code clones exist across service boundaries, some of which are co-modified in the same version. Such clones may require changes to be propagated across service boundaries, thereby undermining service independence and increasing maintenance costs. However, existing tools do not support microservice-aware clone analysis. We present CC4M, a microservice-aware clone analysis and visualization tool. CC4M detects and enriches clone pairs with service-boundary, co-modification, file-category, and metric information. The enriched clones are visualized in an interactive scatter plot with explicit service boundaries, supporting metric-based filtering to prioritize clones with potentially higher maintenance impact. Using an open-source microservice application, we illustrate how CC4M helps identify the potential impact scope of code changes. A demo video and the tool are available at https://www.youtube.com/watch?v=0xOIQPFbkUg and https://doi.org/10.5281/zenodo.21204195, respectively.
Create a lesson
Related papers
ProgramDistill: From Interactive Web Apps to Verifiable Reference-Guided SWE Tasks
Jeonghye Kim, Minseon Kim, Young Jin Kim et al.
Evaluating the Health of Open-Source Smart City Platforms
Rodrigo Bravo Simões, Fernando Brito e Abreu, Vasco Amaral
From Component Snapshots to Lifecycle Traces: Agent-Based Software Composition Analysis
Chaofan Li, Zhengduo Xue, Chengxiang Li et al.
A Study on the Impact of Natural Language Differences in Prompts on Automatic Code Generation Using LLMs
Haruka Tokumasu, Masanari Kondo, Alexander Serebrenik et al.
A Study of the Reliability of Agentic AI-Generated Programs
Ayesha Shafique, Barton P. MIller, Elisa R. Heymann
Relationally Guided Use Case Modeling with LLMs
Guangyu Wang, Bangqi Li, Ji Wu et al.