Autonomous AI agents are transforming software development and redefining how developers collaborate with AI. Prior research shows that the adoption and use of AI-powered tools differ between core and peripheral developers. However, it remains unclear how this dynamic unfolds in the emerging era of autonomous coding agents. In this paper, we present the first empirical study of 9,427 agentic PRs, examining how core and peripheral developers use, review, modify, and verify agent-generated contributions prior to acceptance. Through a mix of qualitative and quantitative analysis, we make four key contributions. First, a subset of peripheral developers use agents more often, delegating tasks evenly across bug fixing, feature addition, documentation, and testing. In contrast, core developers focus more on documentation and testing, yet their agentic PRs are frequently merged into the main/master branch. Second, core developers engage slightly more in review discussions than peripheral developers, and both groups focus on evolvability issues. Third, agentic PRs are less likely to be modified, but when they are, both groups commonly perform refactoring. Finally, peripheral developers are more likely to merge without running CI checks, whereas core developers more consistently require passing verification before acceptance. Our analysis offers a comprehensive view of how developer experience shapes integration offer insights for both peripheral and core developers on how to effectively collaborate with coding agents.

Large language models (LLMs) like ChatGPT have shown the potential to assist developers with coding and debugging tasks. However, their role in collaborative issue resolution is underexplored. In this study, we analyzed 1,152 Developer-ChatGPT conversations across 1,012 issues in GitHub to examine the diverse usage of ChatGPT and reliance on its generated code. Our contributions are fourfold. First, we manually analyzed 289 conversations to understand ChatGPT's usage in the GitHub Issues. Our analysis revealed that ChatGPT is primarily utilized for ideation, whereas its usage for validation (e.g., code documentation accuracy) is minimal. Second, we applied BERTopic modeling to identify key areas of engagement on the entire dataset. We found that backend issues (e.g., API management) dominate conversations, while testing is surprisingly less covered. Third, we utilized the CPD clone detection tool to check if the code generated by ChatGPT was used to address issues. Our findings revealed that ChatGPT-generated code was used as-is to resolve only 5.83% of the issues. Fourth, we estimated sentiment using a RoBERTa-based sentiment analysis model to determine developers' satisfaction with different usages and engagement areas. We found positive sentiment (i.e., high satisfaction) about using ChatGPT for refactoring and addressing data analytics (e.g., categorizing table data) issues. On the contrary, we observed negative sentiment when using ChatGPT to debug issues and address automation tasks (e.g., GUI interactions). Our findings show the unmet needs and growing dissatisfaction among developers. Researchers and ChatGPT developers should focus on developing task-specific solutions that help resolve diverse issues, improving user satisfaction and problem-solving efficiency in software development.

Issue tracking systems serve as the primary tool for incorporating external users and customizing a software project to meet the users' requirements. However, the limited number of contributors and the challenge of identifying the best approach for each issue often impede effective resolution. Recently, an increasing number of developers are turning to AI tools like ChatGPT to enhance problem-solving efficiency. While previous studies have demonstrated the potential of ChatGPT in areas such as automatic program repair, debugging, and code generation, there is a lack of study on how developers explicitly utilize ChatGPT to resolve issues in their tracking system. Hence, this study aims to examine the interaction between ChatGPT and developers to analyze their prevalent activities and provide a resolution. In addition, we assess the code reliability by confirming if the code produced by ChatGPT was integrated into the project's codebase using the clone detection tool NiCad. Our investigation reveals that developers mainly use ChatGPT for brainstorming solutions but often opt to write their code instead of using ChatGPT-generated code, possibly due to concerns over the generation of "hallucinated" code, as highlighted in the literature.

Abstract The hike in the demand for smart cities has gathered the interest of researchers to work on environmental sound classification. Most researchers' goal is to reach the Bayesian optimal error in the field of audio classification. Nonetheless, it is very baffling to interpret meaning from a three‐dimensional audio and this is where different types of spectrograms become effective. Using benchmark spectral features such as mel frequency cepstral coefficients (MFCCs), chromagram, log‐mel spectrogram (LM), and so on audio can be converted into meaningful 2D spectrograms. In this paper, we propose a convolutional neural network (CNN) model, which is fabricated with additive angular margin loss (AAML), large margin cosine loss (LMCL) and a‐softmax loss. These loss functions proposed for face recognition, hold their value in the other fields of study if they are implemented in a systematic manner. The mentioned loss functions are more dominant than conventional softmax loss when it comes to classification task because of its capability to increase intra‐class compactness and inter‐class discrepancy. Thus, with MCAAM‐Net, MCAS‐Net and MCLCM‐Net models, a classification accuracy of 99.60%, 99.43% and 99.37% is achieved on UrbanSound8K dataset respectively without any augmentation. This paper also demonstrates the benefit of stacking features together and the above‐mentioned validation accuracies are achieved after stacking MFCCs and chromagram on the x ‐axis. We also visualized the clusters formed by the embedded vectors of test data for further acknowledgement of our results, after passing it through different proposed models. Finally, we show that the MCAAM‐Net model achieved an accuracy of 99.60% on UrbanSound8K dataset, which outperforms the benchmark models like TSCNN‐DS, ADCNN‐5, ESResNet‐Attention, and so on that are introduced over the recent years.