Yuehan Sun | Searcharxiv

arXiv2026

Characterizing the Landscape of Open-Source Satellite Software

Satellites have become fundamental components of modern technological systems, supporting critical infrastructure in communication, navigation, Earth observation, and scientific research. As space exploration advances and demand for satellite-enabled services grows, reliance on complex, heterogeneous satellite software continues to increase. A systematic understanding of the satellite software landscape is therefore increasingly important, yet existing studies still lack a comprehensive empirical examination. To address this gap, we present the first characterization study of open-source satellite software, examining its ecosystem and development practices. We mine and analyze 22,286 satellite-related GitHub projects through three research questions on popularity trends (RQ1), software goals (RQ2), and development practices (RQ3). First, we characterize the temporal evolution of projects and active developers, revealing increasing popularity. Second, through manual inspection of 646 projects, we construct a taxonomy of 43 software-goal categories spanning conceptual design, datasets, system implementation, simulation, testing, and tools. Third, we conduct an in-depth analysis of projects with source code, revealing a highly heterogeneous and task-specialized ecosystem with 66 programming languages and diverse implementation strategies. Finally, we summarize key findings and derive actionable implications for satellite developers and researchers.

cs.SE↗

arXiv2025

SlsReuse: LLM-Powered Serverless Function Reuse

Serverless computing has rapidly emerged as a popular cloud computing paradigm. It enables developers to implement function-level tasks, i.e., serverless functions, without managing infrastructure. While reducing operational overhead, it poses challenges, especially for novice developers. Developing functions from scratch requires adapting to heterogeneous, platform-specific programming styles, making the process time-consuming and error-prone. Function reuse offers a promising solution to address these challenges. However, research on serverless computing lacks a dedicated approach for function recommendation. Existing techniques from traditional contexts remain insufficient due to the semantic gap between task descriptions and heterogeneous function implementations. Advances in large language models (LLMs), pre-trained on large-scale corpora, create opportunities to bridge this gap by aligning developer requirements with function semantics. This paper presents SlsReuse, the first LLM-powered framework for serverless function reuse. Specifically, SlsReuse first constructs a reusable function repository serving as a foundational knowledge base. Then, it learns unified semantic-enhanced representations of heterogeneous functions through effective prompt engineering with few-shot prompting, capturing implicit code intent, target platforms, programming languages, and cloud services. Finally, given a natural language task query, SlsReuse performs intent-aware discovery combined with a multi-level pruning strategy and similarity matching. We evaluate SlsReuse on a curated dataset of 110 task queries. Built on ChatGPT-4o, one of the most representative LLMs, SlsReuse achieves Recall@10 of 91.20%, exceeding the state-of-the-art baseline by 24.53 percentage points.

cs.SE↗