The rapid adoption of large language models has led to the emergence of AI coding agents that autonomously create pull requests on GitHub. However, how these agents differ in their pull request description characteristics, and how human reviewers respond to them, remains underexplored. In this study, we conduct an empirical analysis of pull requests created by five AI coding agents using the AIDev dataset. We analyze agent differences in pull request description characteristics, including structural features, and examine human reviewer response in terms of review activity, response timing, sentiment, and merge outcomes. We find that AI coding agents exhibit distinct PR description styles, which are associated with differences in reviewer engagement, response time, and merge outcomes. We observe notable variation across agents in both reviewer interaction metrics and merge rates. These findings highlight the role of pull request presentation and reviewer interaction dynamics in human–AI collaborative software development.

The widespread adoption of generative AI (GenAI) tools such as GitHub Copilot and ChatGPT is transforming software development. Since generated source code is virtually impossible to distinguish from manually written code, their real-world usage and impact on opensource software (OSS) development remain poorly understood. In this paper, we introduce the concept of self-admitted GenAI usage, that is, developers explicitly referring to the use of GenAI tools for content creation in software artifacts. Using this concept as a lens to study how GenAI tools are integrated into OSS projects, we analyze a curated sample of more than 200,000 GitHub repositories, identifying 1,292 such self-admissions across 156 repositories in commit messages, code comments, and project documentation. Using a mixed methods approach, we derive a taxonomy of 32 tasks, 10 content types, and 11 purposes associated with GenAI usage based on 1,292 qualitatively coded mentions. We then analyze 13 documents with policies and usage guidelines for GenAI tools and conduct a developer survey to uncover the ethical, legal, and practical concerns behind them. Our findings reveal that developers actively manage how GenAI is used in their projects, highlighting the need for project-level transparency, attribution, and quality control practices in AI-assisted software development. Finally, we examine the longitudinal impact of GenAI adoption on code churn in 151 repositories with self-admitted GenAI usage and find no general increase, contradicting popular narratives on the impact of GenAI on software development.

The Log4j-Core vulnerability, known as Log4Shell, exposed significant challenges to dependency management in software ecosystems. When a critical vulnerability is disclosed, it is imperative that dependent packages quickly adopt patched versions to mitigate risks. However, delays in applying these updates can leave client systems exposed to exploitation. Previous research has primarily focused on NPM, but there is a need for similar analysis in other ecosystems, such as Maven. Leveraging the 2025 mining challenge dataset of Java dependencies, we identify factors influencing update lags and categorize them based on version classification (major, minor, patch release cycles). Results show that lags exist, but projects with higher release cycle rates tend to address severe security issues more swiftly. In addition, over half of vulnerability fixes are implemented through patch updates, highlighting the critical role of incremental changes in maintaining software security. Our findings confirm that these lags also appear in the Maven ecosystem, even when migrating away from severe threats.

Log4j has become a widely adopted logging library for Java programs due to its long history and high reliability. Its widespread use is notable not only because of its maturity but also due to the complexity and depth of its features, which have made it an essential tool for many developers. However, Log4j 1.x, which reached its end of support (deprecated), poses significant security risks and has numerous deprecated features that can be exploited by attackers. Despite this, some clients may still rely on this library. We aim to understand whether clients are still using Log4j 1.x despite its official support ending. We utilized the Mining Software Repositories 2025 challenge dataset, which provides a large and representative sample of open-source software projects. We analyzed over 10,000 log entries from the Mining Software Repositories 2025 challenge dataset using the Goblin framework to identify trends in usage rates for both Log4j 1.x and Log4j-core 2.x. Specifically, our study addressed two key issues: (1) We examined the usage rates and trends for these two libraries, highlighting any notable differences or patterns in their adoption. (2) We demonstrate that projects initiated after a deprecated library has reached the end of its support lifecycle can still maintain significant popularity. These findings highlight how deprecated are still popular, with the next step being to understand the reasoning behind these adoptions.

There is growing concern about maintainers self-sabotaging their work in order to take political or economic stances, a practice referred to as “protestware”. Our objective is to understand the discourse around discussions on such an attack, how it is received by the community, and whether developers respond to the attack in a timely manner. We study two notable protestware cases i.e., colors.js and es5-ext. Results indicate that protestware discussions are spread more quickly on the GitHub platform, while security vulnerabilities are faster on social media. By establishing a taxonomy of protestware discussions, we identify posts that express stances and provide technical mitigation instructions. We applied a thematic analysis to 684 protestware related posts to identify five major themes during the discussions: i. disseminate and response, ii. stance, iii. reputation, iv. communicative styles, v. rights and ethics. This work sheds light on the nuanced landscape of protestware discussions, offering insights for both researchers and developers into maintaining a healthy balance between the political or social actions of developers and the collective well-being of the open-source community.

GitHub Sponsors was launched in 2019, enabling donations to open-source software developers to provide financial support, as per GitHub's slogan: "Invest in the projects you depend on". However, a 2022 study on GitHub Sponsors found that only two-fifths of developers who were seeking sponsorship received a donation. The study found that, other than internal actions (such as offering perks to sponsors), developers had advertised their GitHub Sponsors profiles on social media, such as Twitter (also known as X). Therefore, in this work, we investigate the impact of tweets that contain links to GitHub Sponsors profiles on sponsorship, as well as their reception on Twitter/X. We further characterize these tweets to understand their context and find that (1) such tweets have the impact of increasing the number of sponsors acquired, (2) compared to other donation platforms such as Open Collective and Patreon, GitHub Sponsors has significantly fewer interactions but is more visible on Twitter/X, and (3) developers tend to contribute more to open-source software during the week of posting such tweets. Our findings are the first step toward investigating the impact of social media on obtaining funding to sustain open-source software.