%A NIM.: 21106050023 Ekasari Amalia
%O Muhammad Galih Wonoseto, M.T.
%T RANCANG BANGUN VS CODE EXTENSION UNTUK IDENTIFIKASI KODE YANG BERISIKO MENGHAMBAT MAINTAINABILITY MENGGUNAKAN SOFTWARE METRICS DAN LARGE LANGUAGE MODEL
%X Maintainability is a software quality attribute related to the ease of understanding,
repairing, modifying, and developing code. This study aims to develop the
Maintainability Risk Analyzer as a VS Code Extension to identify maintainability
risks in JavaScript code using software metrics and provide contextual explanations
through a Large Language Model (LLM). The system was developed using Extreme
Programming (XP), consisting of planning, design, coding, and testing. Static
analysis utilizes the Abstract Syntax Tree (AST) to obtain Halstead Volume,
Cyclomatic Complexity, and Lines of Code (LOC), which are used to calculate the
Visual Studio Maintainability Index (MIVS) and determine the risk level. The
analysis results are displayed directly within the development environment through
Diagnostic, Highlight, Hover, and Show Summary. The Explain Maintainability
Risk feature provides contextual explanations of risk factors and LLM-based
refactoring suggestions. The implementation and testing results show that the
extension can perform maintainability analysis on JavaScript code, display risk
levels and the corresponding metrics, and provide explanations and improvement
suggestions through the LLM-based feature. User evaluation of the LLM-based
feature using the Extended TAM among 101 valid respondents resulted in an overall
mean score of 3.49, which falls into the very high category.
%K Visual Studio Code, Extension, Maintainability, Software Metrics,
JavaScript, Large Language Model
%D 2026
%I UIN SUNAN KALIJAGA YOGYAKARTA
%L digilib79613