<mets:mets OBJID="eprint_76864" LABEL="Eprints Item" xsi:schemaLocation="http://www.loc.gov/METS/ http://www.loc.gov/standards/mets/mets.xsd http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-3.xsd" xmlns:mets="http://www.loc.gov/METS/" xmlns:mods="http://www.loc.gov/mods/v3" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"><mets:metsHdr CREATEDATE="2026-06-17T16:30:01Z"><mets:agent ROLE="CUSTODIAN" TYPE="ORGANIZATION"><mets:name>Institutional Repository UIN Sunan Kalijaga Yogyakarta</mets:name></mets:agent></mets:metsHdr><mets:dmdSec ID="DMD_eprint_76864_mods"><mets:mdWrap MDTYPE="MODS"><mets:xmlData><mods:titleInfo><mods:title>RANCANG BANGUN SISTEM ANALISIS DEPENDENSI STATIS DENGAN ANTARMUKA GRAFIS INTERAKTIF</mods:title></mods:titleInfo><mods:name type="personal"><mods:namePart type="given">NIM.: 22106050037</mods:namePart><mods:namePart type="family">Rama Audra Aji Pangestu</mods:namePart><mods:role><mods:roleTerm type="text">author</mods:roleTerm></mods:role></mods:name><mods:abstract>Understanding an unfamiliar codebase is an important challenge in software development, especially when developers are onboarded into a new codebase. This final project aims to design and develop Tauta, a static dependency analysis system with an interactive graphical interface to support program comprehension in JavaScript/TypeScript codebases. The system was developed using an Iterative and Incremental approach through three main focuses: building the foundation of static dependency analysis, developing architectural metrics, and refining a decision-support-oriented interface. Tauta can construct a dependency graph, display dependencies and dependents, detect circular dependencies, identify orphan candidates, and present analysis metrics and signals through a diagnosis-first approach to guide developers’ review priorities. The program comprehension evaluation compared manual task completion with task completion using Tauta based on Task Completion Time (TCT) and Accuracy. The results show that overall Accuracy increased from 25.00% to 100.00%. The total TCT for Task 1 and Task 2 decreased from 595.34 seconds to 202.70 seconds, resulting in 65.95% time savings. In Task 3, all participants failed to identify orphan candidates manually but succeeded when using Tauta. These results indicate that Tauta supports faster and more accurate dependency-structure comprehension in the evaluated scenario.</mods:abstract><mods:classification authority="lcc">005.36 Software Development / Pengembangan Perangkat Lunak</mods:classification><mods:originInfo><mods:dateIssued encoding="iso8061">2026-05-26</mods:dateIssued></mods:originInfo><mods:originInfo><mods:publisher>UIN SUNAN KALIJAGA YOGYAKARTA;FAKULTAS SAINS DAN TEKNOLOGI</mods:publisher></mods:originInfo><mods:genre>Thesis</mods:genre></mets:xmlData></mets:mdWrap></mets:dmdSec><mets:amdSec ID="TMD_eprint_76864"><mets:rightsMD ID="rights_eprint_76864_mods"><mets:mdWrap MDTYPE="MODS"><mets:xmlData><mods:useAndReproduction>
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