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.

AI coding agents contribute thousands of pull requests daily to open-source repositories, yet practitioners lack empirical guidance on optimal contribution sizing. We adapt the therapeutic index framework from pharmacology to characterise the relationship between pull request size and integration outcomes. Analysing 33,078 agent-authored pull requests from the AIDev dataset across five AI coding agents, we model dose-response relationships for efficacy (merge probability) and toxicity (review friction). All agents exhibit statistically significant negative relationships between size and merge probability (p < 0.001), differing substantially in baseline performance (47.5% to 82.6%) and dose sensitivity. Task type substantially moderates these relationships: bug fixes exhibit ED50 of 1,467 lines [95% CI: 1,025–2,216] with therapeutic index of 35.2, whilst features show ED50 of 18,234 lines [95% CI: 12,337–28,390] with therapeutic index of 2.3. This 15-fold difference, confirmed by non-overlapping confidence intervals, indicates that bug fixes exhibit wider therapeutic windows whilst feature implementations exhibit narrower windows.

The rapid evolution of blockchain technology has created a diverse ecosystem of platforms, tools, and applications, and understanding how these technologies are adopted and used in practice is essential for advancing Blockchain-Oriented Software Engineering. This paper presents Web3BlockSet, a curated dataset that enables empirical insights into the blockchain ecosystem through the analysis of Mining Software Repositories on GitHub. We collected 391,596 Issues and Pull Requests from organizations that maintain core blockchain technologies and from community developers who create applications. The dataset offers broad categorization across the blockchain stack, a dual perspective of technology vendors and adopters, and rich metadata that supports diverse analytical approaches.

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.

In online auctions, fraudulent behaviours such as shill bidding pose significant risks. This paper presents a conceptual framework that applies dynamic, behaviour-based penalties to deter auction fraud using blockchain smart contracts. Unlike traditional post-auction detection methods, this approach prevents manipulation in real-time by introducing an economic disincentive system where penalty severity scales with suspicious bidding patterns. The framework employs the proposed Bid Shill Score (BSS) to evaluate nine distinct bidding behaviours, dynamically adjusting the penalty fees to make fraudulent activity financially unaffordable while providing fair competition. The system is implemented within a decentralized English auction on the Ethereum blockchain, demonstrating how smart contracts enforce transparent auction rules without trusted intermediaries. Simulations confirm the effectiveness of the proposed model: the dynamic penalty mechanism reduces the profitability of shill bidding while keeping penalties low for honest bidders. Performance evaluation shows that the system introduces only moderate gas and latency overhead, keeping transaction costs and response times within practical bounds for real-world use. The approach provides a practical method for behaviour-based fraud prevention in decentralised systems where trust cannot be assumed.

Similar to other industries, the software engineering domain is plagued by psychological diseases such as burnout, which lead developers to lose interest, exhibit lower activity and/or feel powerless. Prevention is essential for such diseases, which in turn requires early identification of symptoms. The emotional dimensions of Valence, Arousal and Dominance (VAD) are able to derive a person's interest (attraction), level of activation and perceived level of control for a particular situation from textual communication, such as emails. As an initial step towards identifying symptoms of productivity loss in software engineering, this paper explores the VAD metrics and their properties on 700,000 Jira issue reports containing over 2,000,000 comments, since issue reports keep track of a developer's progress on addressing bugs or new features. Using a general-purpose lexicon of 14,000 English words with known VAD scores, our results show that issue reports of different type (e.g., Feature Request vs. Bug) have a fair variation of Valence, while increase in issue priority (e.g., from Minor to Critical) typically increases Arousal. Furthermore, we show that as an issue's resolution time increases, so does the arousal of the individual the issue is assigned to. Finally, the resolution of an issue increases valence, especially for the issue Reporter and for quickly addressed issues. The existence of such relations between VAD and issue report activities shows promise that text mining in the future could offer an alternative way for work health assessment surveys.

Issue tracking systems store valuable data for testing hypotheses concerning maintenance, building statistical prediction models and (recently) investigating developer affectiveness. For the latter, issue tracking systems can be mined to explore developers emotions, sentiments and politeness---affects for short. However, research on affect detection in software artefacts is still in its early stage due to the lack of manually validated data and tools.

Human Affectiveness, i.e., The emotional state of a person, plays a crucial role in many domains where it can make or break a team's ability to produce successful products. Software development is a collaborative activity as well, yet there is little information on how affectiveness impacts software productivity. As a first measure of this impact, this paper analyzes the relation between sentiment, emotions and politeness of developers in more than 560K Jira comments with the time to fix a Jira issue. We found that the happier developers are (expressing emotions such as JOY and LOVE in their comments), the shorter the issue fixing time is likely to be. In contrast, negative emotions such as SADNESS, are linked with longer issue fixing time. Politeness plays a more complex role and we empirically analyze its impact on developers' productivity.