Projects

ソフトウェア開発プロセス 近年，ソフトウェア開発は大規模化，複雑化の一途をたどっており，巨大なシステムの開発には数千人もの開発者，数十もの開発

現在編集中 しばらくお待ちください．当該テーマにご興味がある方は，sdlab-contact@is.naist.jpにご連絡ください

現在編集中 しばらくお待ちください．当該テーマにご興味がある方は，sdlab-contact@is.naist.jpにご連絡ください

ソフトウェア開発履歴からのお宝発掘 - リポジトリマイニング 本研究ではソフトウェア理解を支援するために，これまでに開発されたソフトウェア群の開発

(現在編集中[2024年5月15日]．最新研究に興味がある方はsdlab-contact@is.naist.jpにご連絡ください) ソフトウェ

近年のクラウドコンピューティングの飛躍的な普及の背景には，ソフトウェア技術により計算機資源を仮想化することで，計算機環境の構築・割当を動的か

概要 PC上では不可能な規模の計算を実現する高性能計算機（スーパコンピュータ）は，天気予報のように身近なところから，ビッグデータ解析や人工知能

To fully utilize multi-core processors, the development of parallel programs is essential. However, creating parallel programs is a challenging and complex task. Automatic parallelization techniques have been extensively studied to simplify this process by transforming sequential code into parallel code automatically. Most existing automatic parallelization tools rely on static analysis, which can identify certain types of parallel structures but fails to detect others, resulting in suboptimal performance gains. In contrast, the recent emergence of Large Language Models (LLMs) in the field of Natural Language Processing (NLP) has inspired software engineering researchers to adopt these models.

Federated learning is a technique for training machine learning models while keeping data private. The data is kept on the devices or servers where it was collected, and only updates to the model are shared between the parties involved in the training process. This approach is effective in addressing privacy concerns and allows for a diverse dataset, with data coming from multiple sources. However, resource constraints on edge devices make it difficult for all user devices to use the same model, which could impact the model’s accuracy.

Virtual Machines (VMs) are used extensively in the cloud. The underlying hypervisors allow hardware resources to be split into multiple virtual units which enables server consolidation, fault containment and resource management. However, VMs with traditional architecture introduce heavy overhead and reduce application performance. Containers are becoming popular options for running applications, yet such a solution raises security concerns due to weaker isolation than VMs. We are at the point of container and traditional virtualization convergence where lightweight hypervisors are implemented and integrated into the container ecosystem in order to maximize the benefits of VM isolation and container performance.