AI 中文总结
针对星链测量数据转换难问题，提出动态编码工具StarCodex，它能检测分析差距并转换为编码任务，利用Codex生成或修复工件。实验表明其能发现多数系统风险案例，精度更高，还揭示了预测风险和QoE风险差异，证明该工具用于自动化实验工作流程的可行性。
AI 中文摘要
星链和其他低地球轨道（LEO）卫星宽带系统正在跨区域、时间段和接入条件产生越来越多样化的测量数据。这些测量对于吞吐量预测、自适应比特率（ABR）评估和网络实验很有价值，但将持续到达的数据转换为可重复使用的实验证据仍严重依赖手动开发的分析代码以及专家指导的数据检查和故障案例组织。本文提出了StarCodex，一种用于星链测量分析和实验自动化的动态编码工具。StarCodex从当前测量状态中检测分析差距，将其转换为结构化编码任务，使用Codex生成或修复可执行分析工件，并通过代码、数据接口、测量语义和输出验证来接受工件。对真实星链测量的实验表明，StarCodex发现了56个未发现的系统风险案例中的49个，比最强的预定义分析基线具有更高的平均精度，并构建了一个具有更密集和更广泛系统风险证据的基准。生成的预测和重放工件进一步揭示了预测风险以及ABR控制器之间的体验质量（QoE）——风险差异。这些结果证明了使用动态编码工具将不断演变的星链测量转换为经过验证的分析工件以实现自动化实验工作流程的可行性。
英文摘要
Starlink and other low Earth orbit (LEO) satellite broadband systems are producing increasingly diverse measurement data across regions, time periods, and access conditions. These measurements are valuable for throughput prediction, adaptive bitrate (ABR) evaluation, and network experimentation, but converting continuously arriving data into reusable experimental evidence still relies heavily on manually developed analysis code and expert-guided data inspection and failure-case organization. This paper proposes StarCodex, a dynamic coding harness for Starlink measurement analysis and experiment automation. StarCodex detects analysis gaps from the current measurement state, converts them into structured coding tasks, uses Codex to generate or repair executable analysis artifacts, and accepts artifacts through code, data-interface, measurement-semantics, and output validation. Experiments on real Starlink measurements show that StarCodex discovers 49 of 56 uncovered system-risk cases, attains higher average precision than the strongest predefined analysis baseline, and constructs a benchmark with denser and broader system-risk evidence. The generated prediction and replay artifacts further reveal prediction risks and quality-of-experience (QoE)--risk differences among ABR controllers. These results demonstrate the feasibility of using a dynamic coding harness to convert evolving Starlink measurements into validated analysis artifacts for automated experiment workflows.
