| LI YAN | M, 2回目発表 | 計算システムズ生物学 | 金谷 重彦, | 松本 健一, | 小野 直亮, | MD.Altaf-Ul-Amin |
| 牛島 知彦 | D, 中間発表 | 計算システムズ生物学 | 金谷 重彦, | 松本 健一, | 小野 直亮, | MD.Altaf-Ul-Amin |
| SUWANACHOTE NABHAN | M, 2回目発表 | ソフトウェア設計学 | 飯田 元, | 松本 健一, | 柏 祐太郎, | Reid Brittany |
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title: Evaluating the impact of Domain Adaptation for Code Completion Models
abstract: Code completion tools are widely used in modern software development environments, with deep learning (DL)-based models offering significant improvements over traditional approaches through their ability to provide context-aware and multi-line suggestions. Recent work has shown that domain adaptation—fine-tuning language models on project-specific or domain-specific code—can further enhance completion quality. However, the specific benefits and behaviors of such adapted models remain underexplored. In this study, we evaluate the impact of domain adaptation on multiple large language models (LLMs) for code completion across several software projects. We examine how domain adaptation affects model performance at the project level. Our findings offer empirical insights into the effectiveness of domain-adapted code completion models and contribute to a deeper understanding of their applicability and limitations. language of the presentation: English | ||||||