Serverless computing is a popular cloud computing paradigm that enables developers to build applications at the function level, known as serverless applications. The Serverless Application Model (AWS SAM) is the most widely adopted configuration schema. However, misconfigurations pose a significant challenge due to the complexity of serverless configurations and the limitations of traditional data-driven techniques. Recent advancements in Large Language Models (LLMs), pre-trained on large-scale public data, offer promising potential for identifying and explaining misconfigurations. In this article, we present SlsDetector , the first framework that harnesses the capabilities of LLMs to perform static misconfiguration detection in serverless applications. SlsDetector utilizes effective prompt engineering with zero-shot prompting to identify configuration issues. It designs multi-dimensional constraints aligned with serverless configuration characteristics and leverages the Chain of Thought technique to enhance LLM inferences, alongside generating structured responses. We evaluate SlsDetector on a curated dataset of 110 configuration files, which includes correct configurations, real-world misconfigurations, and intentionally injected errors. Our results show that SlsDetector , based on ChatGPT-4o (one of the most representative LLMs), achieves a precision of 72.88%, recall of 88.18%, and F1-score of 79.75%, outperforming state-of-the-art data-driven methods by 53.82, 17.40, and 49.72 percentage points, respectively. We further investigate the generalization capability of SlsDetector across recent LLMs, including Llama 3.1 (405B) Instruct Turbo, Gemini 1.5 Pro, and DeepSeek V3, with consistently high effectiveness.

Satellite computing is an emerging paradigm that empowers satellites to perform onboard processing tasks (i.e., satellite applications), thereby reducing reliance on ground-based systems and improving responsiveness. However, enabling application software updates in this context remains a fundamental challenge due to application heterogeneity, limited ground-to-satellite bandwidth, and harsh space conditions. Existing software update approaches, designed primarily for terrestrial systems, fail to address these constraints, as they assume abundant computational capacity and stable connectivity.To address this gap, we propose SateLight, a practical and effective satellite application update framework tailored for satellite computing. SateLight leverages containerization to encapsulate heterogeneous applications, enabling efficient deployment and maintenance. SateLight further integrates three capabilities: (1) a content-aware differential strategy that minimizes communication data volume, (2) a fine-grained onboard update design that reconstructs target applications, and (3) a layer-based fault-tolerant recovery mechanism to ensure reliability under failure-prone space conditions. Experimental results on a satellite simulation environment with 10 representative satellite applications demonstrate that SateLight reduces transmission latency by up to 91.18% (average 56.54%) compared to the best currently available baseline. It also consistently ensures 100% update correctness across all evaluated applications. Furthermore, a case study on a real-world in-orbit satellite demonstrates the practicality of our approach.