Modern scientific workflows are data-driven and are often executed on distributed, heterogeneous, high-performance computing infrastructures. Anomalies and failures in the workflow execution cause loss of scientific productivity and inefficient use of the infrastructure. Hence, detecting, diagnosing, and mitigating these anomalies are immensely important for reliable and performant scientific workflows. Since these workflows rely heavily on high-performance network transfers that require strict QoS constraints, accurately detecting anomalous network performance is crucial to ensure reliable and efficient workflow execution. To address this challenge, we have developed X-FLASH, a network anomaly detection tool for faulty TCP workflow transfers. X-FLASH incorporates novel hyperparameter tuning and data mining approaches for improving the performance of the machine learning algorithms to accurately classify the anomalous TCP packets. X-FLASH leverages XGBoost as an ensemble model and couples XGBoost with a sequential optimizer, FLASH, borrowed from search-based Software Engineering to learn the optimal model parameters. X-FLASH found configurations that outperformed the existing approach up to 28\%, 29\%, and 40\% relatively for F-measure, G-score, and recall in less than 30 evaluations. From (1) large improvement and (2) simple tuning, we recommend future research to have additional tuning study as a new standard, at least in the area of scientific workflow anomaly detection.
Mining Workflow Graphs for Black-Box Boundary Testing of Conversational LLM Agents
Lero, the Research Ireland Centre for Software University of Limerick（利罗爱尔兰软件研究中心，利默里克大学）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；University of Ottawa（渥太华大学）；Genesys Ireland
Learning Service Selection Decision Making Behaviors During Scientific Workflow Development
Southern Methodist University（南卫理公会大学）；Marshall Space Flight Center, NASA（美国国家航空航天局马歇尔太空飞行中心）；Science Mission Directorate, NASA Headquarters（美国国家航空航天局总部科学任务局）；Jet Propulsion Laboratory, NASA（美国国家航空航天局喷气推进实验室）
World of Code: Enabling a Research Workflow for Mining and Analyzing the Universe of Open Source VCS data
开放源码软件对现代社会至关重要，虽然对个别（通常是核心）项目进行了大量研究，但对整个开放源码软件生态系统的外围只有有限的了解。比如，周边的几千万项目是如何通过互联互通的。技术依赖、代码共享还是知识流？为了回答这些问题，我们：a）在整个FLOSS生态系统中创建一个非常大且经常更新的版本控制数据集合，名为World of Code（WoC），可以完全交叉引用FLOSS生态系统的作者，项目，提交，blob，依赖关系和历史; b）提供有效纠正，增强，查询和分析数据的能力。我们目前的WoC实现能够每月更新，包含超过18 B的Git对象。为了评估其研究潜力，并为它的使用创造小插曲，我们聘请了妇委会进行几项研究任务。特别是，我们发现，它能够支持趋势评估，生态系统的测量，并确定包的使用。我们期望妇委会能促进对开放源码软件发展的全球特性的研究，从而提高整个开放源码软件生态系统的弹性。我们的基础设施有助于发现关键的技术依赖关系、代码流和社交网络，这些网络为确定推动FLOSS活动和创新的关系的结构和演变提供了基础。
