relation: https://digilib.uin-suka.ac.id/id/eprint/79613/
title: RANCANG BANGUN VS CODE EXTENSION UNTUK IDENTIFIKASI KODE YANG BERISIKO MENGHAMBAT MAINTAINABILITY MENGGUNAKAN SOFTWARE METRICS DAN LARGE LANGUAGE MODEL
creator: Ekasari Amalia, NIM.: 21106050023
subject: 005.12 Software System Analysis and Design/Sistem Analisa dan Desain Perangkat Lunak
description: 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.
date: 2026-08-28
type: Thesis
type: NonPeerReviewed
format: text
language: id
identifier: https://digilib.uin-suka.ac.id/id/eprint/79613/1/21106050023_BAB-I_IV-atau-V_DAFTAR-PUSTAKA.pdf
format: text
language: id
identifier: https://digilib.uin-suka.ac.id/id/eprint/79613/2/21106050023_BAB-II_sampai_SEBELUM-BAB-TERAKHIR.pdf
identifier:   Ekasari Amalia, NIM.: 21106050023  (2026) RANCANG BANGUN VS CODE EXTENSION UNTUK IDENTIFIKASI KODE YANG BERISIKO MENGHAMBAT MAINTAINABILITY MENGGUNAKAN SOFTWARE METRICS DAN LARGE LANGUAGE MODEL.  Skripsi thesis, UIN SUNAN KALIJAGA YOGYAKARTA.