大型語言模型程式碼重構能力之實證研究
大型語言模型 (LLM) 如 StarCoder2 在自動化程式碼重構方面展現出巨大潛力，尤其在減少程式碼異味和改善程式碼品質指標方面，但開發人員在處理複雜設計問題和需要深入程式碼理解的任務上仍然表現更出色。
대규모 언어 모델의 코드 리팩토링 성능에 대한 실증 연구
대규모 언어 모델(LLM)은 코드 리팩토링 작업을 자동화하여 코드 품질을 향상시키는 데 효과적이며, 특히 구문 및 패턴 기반 코드 냄새를 줄이는 데 탁월하지만, 복잡한 디자인 문제 해결에는 개발자의 전문 지식이 여전히 필요합니다.
大規模言語モデルのコードリファクタリング能力に関する実証的研究
大規模言語モデルStarCoder2は、開発者が書いたコードと同じコードに対して、コードスメルの削減やコード品質の改善において、多くの場合、開発者よりも優れたパフォーマンスを発揮する。
Evaluating the Code Refactoring Capabilities of StarCoder2: An Empirical Study Comparing LLM-Generated Refactorings to Developer-Driven Refactorings
Large Language Models (LLMs), specifically StarCoder2, demonstrate promising capabilities in automating code refactoring, often surpassing human developers in reducing code smells and improving certain code quality metrics, but still face challenges in replicating the contextual understanding and complex decision-making of experienced developers.
Evaluating the Ability of OpenAI's GPT Large Language Models to Generate Verifiable Specifications in VeriFast for Heap-Manipulating C Code
While promising for automating the laborious task of writing specifications for static verification tools, OpenAI's GPT models, even with advanced prompting techniques, still struggle to consistently generate fully correct and verifiable specifications in VeriFast for heap-manipulating C code.
大規模多言語コードデバッグベンチマーク：MDEVALの紹介とオープンソースモデルとクローズドソースモデルの性能比較
本稿では、新しい大規模多言語コードデバッグベンチマークであるMDEVALを提案し、自動プログラム修復、コードレビュー、バグ識別の3つのタスクにおけるオープンソースモデルとクローズドソースモデルのデバッグ性能を評価した。
Continuous Analysis: Enhancing Reproducibility in Scientific Research by Extending DevOps Practices
Continuous Analysis, an extension of DevOps practices, enhances the reproducibility of scientific research by incorporating version control, automated workflows, and comprehensive feedback mechanisms throughout the research lifecycle.
The Impact of DevOps Practices on R&D Efficiency and Source Code Management in Large-Scale Enterprises: A Qualitative Case Study Analysis
DevOps practices, particularly Continuous Integration/Continuous Delivery (CI/CD) and robust Source Code Management (SCM), significantly enhance R&D efficiency and software delivery success in large-scale enterprises, despite challenges such as cultural resistance and tool integration complexities.
Linux 核心回歸錯誤修復時間之實證分析：快速修復與故障驅動程式
Linux 核心回歸錯誤的修復時間比先前報告的要快，設備驅動程式是回歸錯誤最容易出現的子系統，但代碼審查和測試實務並不能解釋修復時間的差異。
The Diminishing Returns of Prompt Engineering with Advanced Language Models in Software Engineering Tasks
While advanced language models demonstrate strong potential in software engineering tasks, the effectiveness of traditional prompt engineering techniques diminishes with their use, particularly for reasoning models.
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