TY  - THES
N1  - Ir. Aulia Faqih Rifa?I, M.Kom.
ID  - digilib74874
UR  - https://digilib.uin-suka.ac.id/id/eprint/74874/
A1  - Fansuri Fadel Fitrah Prakon, NIM.: 21106050091
Y1  - 2025/12/16/
N2  - Software testing is a critical phase in the software development lifecycle to
ensure system quality and reliability. However, manual unit test writing remains
time-consuming and effort-intensive, often becoming a burden for developers. This
study aims to design and develop a Visual Studio Code extension named Unittest
Generator, integrated with artificial intelligence based on Large Language Models
(LLMs) through Groq API and OpenRouter API, to automate unit test generation
for multiple programming languages, namely Python, JavaScript, and Java. The
development process applies the Extreme Programming (XP) methodology
combined with a Research and Development (R&D) approach across two
development iterations.
The testing results in the second iteration indicate that all functional
requirements of the system have been successfully fulfilled. The quality evaluation
of the generated unit tests shows line coverage ranging from 85?92% and mutation
scores between 70?86%, indicating that the generated tests are effective in
detecting faults within the source code. Furthermore, evaluation using BLEU and
ROUGE metrics demonstrates a strong structural and semantic alignment between
the generated unit tests and reference implementations. In terms of performance,
the system achieves an average unit test generation time of 4?8 seconds, which is
significantly faster than manual test creation. Based on these results, this research
concludes that the Unittest Generator extension effectively improves testing
efficiency, quality, and developer productivity, and serves as a practical solution to
support AI adoption in modern software testing workflows.
PB  - UIN SUNAN KALIJAGA YOGYAKARTA
KW  - Generasi Unit Test
KW  -  Ekstensi VSCode
KW  -  Large Language Model
KW  - 
Otomatisasi Pengujian
KW  -  Extreme Programming
M1  - skripsi
TI  - RANCANG BANGUN EKSTENSI VISUAL STUDIO CODE (VSCODE) TERINTEGRASI AI UNTUK GENERASI UNIT TEST
AV  - restricted
EP  - 95
ER  -