RANCANG BANGUN VISUAL STUDIO CODE EXTENSION CODEVA UNTUK CODE GENERATOR JAVA SECARA OTOMATIS MENGGUNAKAN LARGE LANGUAGE MODELS (LLMs)

Muhammad Arif Rakhman Azizi, NIM.: 21106050042 (2025) RANCANG BANGUN VISUAL STUDIO CODE EXTENSION CODEVA UNTUK CODE GENERATOR JAVA SECARA OTOMATIS MENGGUNAKAN LARGE LANGUAGE MODELS (LLMs). Skripsi thesis, UIN SUNAN KALIJAGA YOGYAKARTA.

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Abstract

The rapid advancement of technology has spurred a significant demand for software, yet the Coding phase, particularly in a complex language like Java, remains a time-consuming and error-prone process. To address this, a Visual Studio Code Extension named CodeVa was developed to automatically generate Java code using Large Language Models (LLMs). Developed using the Agile Extreme Programming method ology, CodeVa integrates a fine-tuned Open-source model, Qwen2.5 Instruct 7B. This model was specifically trained for Java code generation using the Parameter-Efficient Finetuning (PEFT) technique, Low-Rank Adaptation (LoRA), and the Unsloth framework. The fine-tuning process resulted in substantial improvements across various performance metrics, including an increase in the CodeBLEU score from 0.3204 to 0.3521 and significant gains in ROUGE scores ROUGE-1 from 0.1736 to 0.3705, ROUGE-2 from 0.0251 to 0.2278, and ROUGE-L from 0.0991 to 0.3252 and Pass@10 using kulal version and chen version from 73.17% to 73.78%. Usability testing of CodeVa yielded a System Usability Scale (SUS) score of 81.0, categorizing it as "excellent" and "acceptable." This indicates that CodeVa is a highly effective tool that can significantly enhance developer productivity by facilitating faster and more efficient Java Coding .

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