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Artificial intelligence (AI), including large language models and generative AI, is emerging as a significant force in software development, offering developers powerful tools that span the entire development lifecycle. Although software engineering research has extensively studied AI tools in software development, the specific types of interactions between developers and these AI-powered tools have only recently begun to receive attention. Understanding and improving these interactions has the potential to enhance productivity, trust, and efficiency in AI-driven workflows. In this paper, we propose a taxonomy of interaction types between developers and AI tools, identifying eleven distinct interaction types, such as auto-complete code suggestions, command-driven actions, and conversational assistance. Building on this taxonomy, we outline a research agenda focused on optimizing AI interactions, improving developer control, and addressing trust and usability challenges in AI-assisted development. By establishing a structured foundation for studying developer-AI interactions, this paper aims to stimulate research on creating more effective, adaptive AI tools for software development. 

Effectively onboarding newcomers is essential for the success of open source projects. These projects often provide onboarding guidelines in their ‘CONTRIBUTING’ files (e.g., CONTRIBUTING.md on GitHub). These files explain, for example, how to find open tasks, implement solutions, and submit code for review. However, these files often do not follow a standard structure, can be too large, and miss barriers commonly found by newcomers. In this paper, we propose an automated approach to parse these CONTRIBUTING files and assess how they address onboarding barriers. We manually classified a sample of files according to a model of onboarding bar- riers from the literature, trained a machine learning classifier that automatically predicts the categories of each paragraph (precision: 0.655, recall: 0.662), and surveyed developers to investigate their perspective of the predictions’ adequacy (75% of the predictions were considered adequate). We found that CONTRIBUTING files typically do not cover the barriers newcomers face (52% of the analyzed projects missed at least 3 out of the 6 barriers faced by newcomers; 84% missed at least 2). Our analysis also revealed that information about choosing a task and talking with the community, two of the most recurrent barriers newcomers face, are neglected in more than 75% of the projects. We made available our classifier as an online service that analyzes the content of a given CONTRIBUTING file. Our approach may help community builders identify missing information in the project ecosystem they maintain and newcomers can understand what to expect in CONTRIBUTING files. 

Abstract Software projects frequently use automation tools to perform repetitive activities in the distributed software development process. Recently, GitHub introducedGitHub Actions, a feature providing automated workflows for software projects. Understanding and anticipating the effects of adopting such technology is important for planning and management. Our research investigates how projects useGitHub Actions, what the developers discuss about them, and how project activity indicators change after their adoption. Our results indicate that 1,489 out of 5,000 most popular repositories (almost 30% of our sample) adoptGitHub Actionsand that developers frequently ask for help implementing them. Our findings also suggest that the adoption ofGitHub Actionsleads to more rejections of pull requests (PRs), more communication in accepted PRs and less communication in rejected PRs, fewer commits in accepted PRs and more commits in rejected PRs, and more time to accept a PR. We found similar results when segmenting our results by categories ofGitHub Actions. We suggest practitioners consider these effects when adoptingGitHub Actionson their projects. 

Open source software (OSS) development offers a unique opportunity for students in Software Engineering to experience and participate in large-scale software development, however, the impact of such courses on students' self-efficacy and the challenges faced by students are not well understood. This paper aims to address this gap by analyzing data from multiple instances of OSS development courses at universities in different countries and reporting on how students' self-efficacy changed as a result of taking the course, as well as the barriers and challenges faced by students. 

Automated tools are frequently used in social coding repositories to perform repetitive activities that are part of the distributed software development process. Recently, GitHub introduced GitHub Actions, a feature providing automated workflows for repository maintainers. Although several Actions have been built and used by practitioners, relatively little has been done to evaluate them. Understanding and anticipating the effects of adopting such kind of technology is important for planning and management. Our research is the first to investigate how developers use Actions and how several activity indicators change after their adoption. Our results indicate that, although only a small subset of repositories adopted GitHub Actions to date, there is a positive perception of the technology. Our findings also indicate that the adoption of GitHub Actions increases the number of monthly rejected pull requests and decreases the monthly number of commits on merged pull requests. These results are especially relevant for practitioners to understand and prevent undesirable effects on their projects. 

Open Source Software (OSS) has changed drastically over the last decade, with OSS projects now producing a large ecosystem of popular products, involving industry participation, and providing professional career opportunities. But our field’s understanding of what motivates people to contribute to OSS is still fundamentally grounded in studies from the early 2000s. With the changed landscape of OSS, it is very likely that motivations to join OSS have also evolved. Through a survey of 242 OSS contributors, we investigate shifts in motivation from three perspectives: (1) the impact of the new OSS landscape, (2) the impact of individuals’ personal growth as they become part of OSS communities, and (3) the impact of differences in individuals’ demographics. Our results show that some motivations related to social aspects and reputation increased in frequency and that some intrinsic and internalized motivations, such as learning and intellectual stimulation, are still highly relevant. We also found that contributing to OSS often transforms extrinsic motivations to intrinsic, and that while experienced contributors often shift toward altruism, novices often shift toward career, fun, kinship, and learning. OSS projects can leverage our results to revisit current strategies to attract and retain contributors, and researchers and tool builders can better support the design of new studies and tools to engage and support OSS development.