Large Language Model (LLM) Agents are advancing quickly, with the increasing leveraging of LLM Agents to assist in development tasks such as code generation. While LLM Agents accelerate code generation, studies indicate they may introduce adverse effects on development. However, existing metrics solely measure pass rates, failing to reflect impacts on long-term maintainability and readability, and failing to capture human intuitive evaluations of PR. To increase the comprehensiveness of this problem, we investigate and evaluate the characteristics of LLM to know the pull requests’ characteristics beyond the pass rate. We observe the code quality and maintainability within PRs based on code metrics to evaluate objective characteristics and developers’ reactions to the pull requests from both humans and LLM’s generation. Evaluation results indicate that LLM Agents frequently disregard code reuse opportunities, resulting in higher levels of redundancy compared to human developers. In contrast to the quality issues, our emotions analysis reveals that reviewers tend to express more neutral or positive emotions towards AI-generated contributions than human ones. This disconnect suggests that the surface-level plausibility of AI code masks redundancy, leading to the silent accumulation of technical debt in real-world development environments. Our research provides insights for improving human-AI collaboration.

Understanding the causes of software defects is essential for reliable software maintenance and ecosystem stability. However, existing bug datasets do not distinguish between issues originating within a project from those caused by external dependencies or environmental factors. In this paper we present InEx-Bug, a manually annotated dataset of 377 GitHub issues from 103 NPM repositories, categorizing issues as Intrinsic (internal defect), Extrinsic (dependency/environment issue), Not-a-Bug, or Unknown. Beyond labels, the dataset includes rich temporal and behavioral metadata such as maintainer participation, code changes, and reopening patterns. Analyses show Intrinsic bugs resolve faster (median 8.9 vs 10.2 days), are close more often (92% vs 78%), and require code changes more frequently (57% vs 28%) compared to Extrinsic bugs. While Extrinsic bugs exhibit higher reopen rates (12% vs 4%) and delayed recurrence (median 157 vs 87 days). The dataset provides a foundation for further studying Intrinsic and Extrinsic defects in the NPM ecosystem.

Mentorship in Open Source Software (OSS) projects is crucial for reducing barriers to entry for newcomers and for fostering the technical and social integration of new contributors. While mentorship in OSS has been recognized as essential for sustainable project growth, quantitative research supporting qualitative findings is not common. To address this gap, we present OpenMent, a comprehensive dataset comprising over 500,000 issue comments, pull request comments, and commit messages from GitHub projects participating in the Google Summer of Code (GSoC) program. OpenMent is curated to capture role-specific interactions and communication patterns between mentors and mentees, providing information on the challenges and dynamics of OSS mentoring. This dataset is designed to be a reusable resource for the Software Engineering community, enabling researchers and practitioners to explore mentorship dynamics and investigate the impact of mentoring on contributor retention. By making OpenMent openly available, we aim to facilitate future research in OSS mentorship, fostering a deeper understanding of mentorship challenges, strategies, and contributions to the growth and inclusivity of OSS ecosystems.