We analyse contributor coordination in Kubernetes, a large open source project comparable in scale to a major enterprise software division. Over 11 years, 25,953 contributors created 46,768 issues and 339,332 comments, providing a complete record of collaboration rarely visible in proprietary settings. We examine how contributors organise themselves across technical domains and how coordination affects development outcomes. Our results show that collaboration is highly modular, with contributors interacting 42.9 times more often within domains than across them. Issues related to multiple domains take 4.19 times longer to resolve (due to both coordination complexity and issue difficulty), and coordination within the modules depends on a small subset of contributors. These findings demonstrate how large-scale coordination dependencies can be observed and quantified in open source development, offering a transferable tool for analyzing enterprise-scale software projects.

We present MKR: Mining Kubernetes Repositories, a dataset capturing more than eleven years of development and community interaction in Kubernetes—an open-source platform for automating the deployment, scaling, and management of containerized applications. As the infrastructure backbone for running thousands of applications across diverse environments, Kubernetes has become one of the most widely adopted and influential projects in modern cloud-native computing. Spanning from June 2014 to July 2025, MKR integrates over two million artefacts from GitHub, including 130,832 commits (through July 2025), 83,368 pull requests, 46,768 issues, and 1,795,423 comments (through March 2025). With contributions from 28,890 unique GitHub commenters and 4,931 commit authors, MKR provides a longitudinal record of how Kubernetes has evolved, scaled, and been maintained over time. The dataset supports research on code evolution, long-term maintenance practices such as API deprecation, contributor retention, governance, and the role of automation in development. MKR allows analyses that connect technical change with decision-making, offering a resource for examining the social and technical dimensions of large-scale open source projects.

Context: Open-source software (OSS) development is often studied as a decentralized process driven by technical goals. However, mature OSS projects operate under external constraints such as security advisories, release deadlines, and ecosystem dependencies. These pressures shape technical decisions and also communication patterns among contributors, including emotional expression. Objective: This study investigates how emotional expression in OSS projects varies across different types of repositories, evolves over time, and relates to the activity of top contributors. The goal is to assess whether emotional dynamics are shaped more by project function than by technical domain or project size. Methods: We analyzed issue comments from 14 OSS repositories spanning over ten years. A transformer-based classifier was used to detect emotions. Emotional patterns were quantified using a composite Emotional Index, and contextual activity. Contributor roles were assessed using a Contribution Index combining code activity, discussion engagement, and sustained involvement. Analyses were conducted at the repository, temporal, and contributor levels. Results: The four most frequent emotions across all repositories were gratitude, curiosity, confusion, and approval. Emotional patterns tend to cluster by functional role rather than technical domain, with repositories converging toward stable emotional profiles over time. High-impact contributors show distinct expression patterns that reflect their role and stage of engagement. Conclusion: Emotional expression in OSS projects follows recurring patterns linked to project function, contributor roles, and maturity. These findings can help anticipate communication challenges during project evolution and support interaction strategies among contributor groups with differing emotional tendencies.

Decentralised finance (DeFi) protocols often claim to implement decentralised governance via mechanisms such as decentralised autonomous organisations (DAOs), yet the structure of their development processes is rarely examined in detail. This study presents an in-depth case analysis of the development activity distribution in Lido, a prominent DeFi liquid staking protocol. We analyse 6741 human-generated GitHub actions recorded from September 2020 to February 2025. Using standard inequality metrics – Gini coefficient and Herfindahl–Hirschman Index – alongside contributors’ interaction network and core–periphery modelling, we find that development activity is highly concentrated. Overall, the weighted Gini coefficient reaches 0.82 and the most active contributor alone accounts for 24% of the total activity. Despite an even split between core and peripheral contributors, the core group accounts for 98.1% of all weighted development actions. The temporal analysis shows an increase in concentration over time, with the Gini coefficient rising from 0.686 in the bootstrap phase to 0.817 in the maturity phase. The contributors’ interaction network analysis reveals a hub-and-spoke structure with high centralisation in communication flows. While a case study of a single protocol, Lido represents a critical test of decentralisation claims given its prominence, maturity, and DAO governance structure. These findings demonstrate that open-source DeFi development can exhibit highly concentrated control patterns despite decentralised governance mechanisms, revealing a persistent gap between governance and operational decentralisation.

We analyze developer activity across 10 major Ethereum repositories (totaling 129884 commits, 40550 issues) spanning 10 years to examine how events such as technical upgrades, market events, and community decisions impact development. Through statistical, survival, and network analyses, we find that technical events prompt increased activity before the event, followed by reduced commit rates afterwards, whereas market events lead to more reactive development. Core infrastructure repositories like Go-Ethereum exhibit faster issue resolution compared to developer tools, and technical events enhance core team collaboration. Our findings show how different types of events shape development dynamics, offering insights for project managers and developers in maintaining development momentum through major transitions. This work contributes to understanding the resilience of development communities and their adaptation to ecosystem changes.

We introduce a novel large-scale deep learning model for Limit Order Book mid-price changes forecasting, and we name it ‘HLOB’. This architecture (i) exploits the information encoded by an Information Filtering Network, namely the Triangulated Maximally Filtered Graph, to unveil deeper and non-trivial dependency structures among volume levels; and (ii) guarantees deterministic design choices to handle the complexity of the underlying system by drawing inspiration from the groundbreaking class of Homological Convolutional Neural Networks. We test our model against 9 state-of-the-art deep learning alternatives on 3 real-world Limit Order Book datasets, each including 15 stocks traded on the NASDAQ exchange, and we systematically characterize the scenarios where HLOB outperforms state-of-the-art architectures. Our approach sheds new light on the spatial distribution of information in Limit Order Books and on its degradation over increasing prediction horizons, narrowing the gap between microstructural modeling and deep learning-based forecasting in high-frequency financial markets.

Blockchain technology faces significant challenges related to sustainability, including issues with optimisation, as well as high energy and gas consumption—factors that developers may sometimes neglect. We introduce a methodology to analyse the key sustainability topics discussed by Go-Ethereum developers, using thematic analysis of their issues and comments from Github. Our approach uses the BERT model to conduct an in-depth topic analysis, enabling us to study the underlying themes and trends in developer’s conversations regarding energy use and sustainability. We assess the sustainability of the identified topics using the five dimensions outlined in the Sustainability Awareness Framework (SusAF): economic, social, individual, environmental, and technical. Our goal is to shed light on how much attention developers pay to sustainability and energy consumption issues. The findings from this qualitative analysis aim to encourage technologists to incorporate these considerations into their future projects, in order to achieve better outcomes in terms of sustainability and reduced consumption.