This work presents the first characterization study of open-source satellite software, examining its ecosystem and development practices, revealing a highly heterogeneous and task-specialized ecosystem with 66 programming languages and diverse implementation strategies.
Abstract
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.
Energy consumption and climate change have made sustainability critical in Software Engineering (SE), driving the emergence of Green SE. Over the past 15 years, numerous solutions for sustainable software systems have been published by the SE community, offering a rich resource for analyzing the field's evolution. To explore this, we conducted a systematic mapping study of Green SE research published between 2010 and 2024. We collected 390 publications, categorizing them by application domain (e.g., mobile, cloud, AI) and research type (e.g., optimisation study, benchmarking, literature review Additionally, we analyzed a representative subset of 79 papers to classify the key elements-such as hardware, measurement, stability, and replicability-considered during energy measurement experiments. Our findings indicate that SE conferences host the majority of energy-related literature. Notably, Green SE studies surged in popularity starting in 2023, largely driven by AI-related publications. Optimization and benchmarking emerged as the most prevalent research types. Ultimately, we aim to inform the SE community about current approaches to energy concerns, highlight critical experimental practices, and advocate for continued action toward more sustainable software engineering.
Max Hort, Maria Kechagia, Federica Sarro· 0 citations
Abstract. The integration of internet technologies and geographic information systems (GIS) has positioned Web Map Services(WMS) as indispensable tools for daily life, with their service quality attracting significant attention. This study proposes a quality evaluation model and method tailored for WMS, encompassing four critical dimensions: query and retrieval, map display, thematic services, and productization services. Empirical validation was conducted through functional, performance, and productization evaluations of three leading domestic platforms, utilizing technical benchmarks and user-centric metrics. Results demonstrate the model’s efficacy in quantifying service quality, aligning closely with real-world user experiences. The framework provides actionable guidelines for regulatory bodies to monitor service compliance and for providers to optimize architectural designs, thereby addressing gaps in personalization and cross-border functionalities observed in current systems. Furthermore, this work highlights the necessity of integrating emerging technologies—such as real-time traffic data and AI-driven personalization—to meet evolving demands for energy efficiency, global connectivity, and hyper-localized services. By bridging technical assessments with practical governance needs, the study offers a strategic roadmap for advancing service quality, supporting the development of China’s digital economy, and enhancing societal well-being through reliable geospatial solutions.
Wenli Han, J. Ge, Libo Zhang et al.· The International Archives o...· 0 citations
It is demonstrated that multiple architectural views, continuous monitoring, domain-driven decomposition, and AI-assisted design techniques contribute significantly to improving scalability, maintainability, adaptability, and long-term software sustainability.
A large-scale empirical study of mainstream open-source agent frameworks from an engineering perspective, providing empirical evidence linking framework design choices to engineering risks and highlighting the need for stronger guidance and support in agent framework development.
Yibo Zhai, Junjun Si, Yan Wang et al.· SIGSOFT FSE Companion· 0 citations
Although “feature” is a common term across requirements engineering, product-line engineering, and agile methods, its operationalization in practice–especially through feature branches–has received little empirical attention. Feature branches are widely recommended on platforms like GitHub; yet, empirical evidence on how they are actually used is scarce. This study investigates feature-branch development practices in open-source software (OSS) projects. We conducted a large-scale empirical analysis of 27,103 feature branches from 3,388 popular GitHub repositories. Using branch-naming conventions, we identified feature branches and measured their lifespan, commit history, and commit-share distributions among contributors. In addition, we profiled contributors according to their opened issues, opened pull requests, number of followers, and total stars. Our analysis revealed that feature branches in open-source projects are long-lived, with a median lifespan exceeding two years, diverging from the short, agile iterations typically employed in software development. Commit intensity is low and irregular, averaging fewer than two commits per day. Contribution is highly unequal within feature branches: a small share of contributors accounts for most commits, revealing strong dependence on a small subset of developers. These findings have several practical implications. Our findings suggest that monitoring contribution concentration within feature branches– for instance, by computing the share of commits attributable to the top contributor or a Gini coefficient over the branch’s commit history–can help maintainers identify branches where development knowledge is held by very few individuals. Likewise, rolling commit intensity–computable directly from commit timestamps–may serve as an early signal of branch stagnation in OSS projects. Moreover, these findings establish an empirical baseline for future studies, including analyses of proprietary repositories.
Nitish Patkar, Aimen Fahmi, Timo Kehrer et al.· Empirical Software Engineeri...· 0 citations
Geospatial technology has evolved from a set of specialised mapping tools into an integrated decision infrastructure that links Earth observation, geographic information systems, satellite positioning, uncrewed aerial systems, volunteered geographic information, cloud computing and geospatial artificial intelligence. This critical narrative review examines how that integration is changing evidence production and decision support across agriculture, environmental and biodiversity monitoring, disaster risk management, urban and transport planning, and public health. Literature published primarily from 2000 to 9 June 2026 was selected through live web-based scholarly discovery, DOI and bibliographic verification, citation chaining and targeted searches of accessible scholarly records. The evidence indicates that geospatial systems are most valuable when they combine complementary observations across scales rather than relying on a single sensor, platform or algorithm. Their strongest contributions are spatially explicit monitoring, prioritisation, scenario analysis and repeated observation, while the weakest parts of many workflows remain ground-reference quality, uncertainty propagation, transferability, interoperability and evaluation against operational outcomes. Cloud platforms and deep learning have expanded computational reach, but they can also conceal provenance, amplify geographic bias and encourage benchmark-driven optimisation that does not translate reliably across places. Volunteered data and urban digital twins extend participation and real-time representation, yet introduce uneven coverage, privacy, governance and accountability concerns. The review argues that future progress depends less on incremental accuracy gains than on reproducible multi-source workflows, explicit uncertainty, trustworthy and explainable geospatial artificial intelligence, privacy-preserving governance, interoperable standards and evaluation in the institutions that ultimately use the evidence. Geospatial technology should therefore be judged not only by spatial resolution or predictive performance, but by whether it produces defensible, equitable and actionable knowledge across heterogeneous real-world settings.
Yashvardhan Singh, Divya Singh, Sakshi Shukla et al.· Advances in Research· 0 citations
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