New technologies often change how an individual performs work, such as how generative AI (GenAI) can help a developer write code. New technologies can also impact how people interact with one another, such as how GenAI’s ability to summarize API documentation can reduce the need for developers to ask each other technical questions. In this paper, we report on a two-phase mixed-method study exploring how GenAI influences how humans interact in software development. During phase one, 30 industrial software developers provided data over a period of 5 to 12 days as they worked, contributing 627 experience sampling responses and 207 end-of-workday survey responses. To gain further insight into their work, we interviewed 22 of these developers. During phase two, 131 additional professional developers responded to a survey to explore whether and how the results from phase one are seen across a larger population. Our analysis of the data found that (1) the ability of GenAI to help answer low-level technical questions in a timely way enables developers to see GenAI as a technical mentor, providing opportunities for developers to turn to tools rather than teammates; (2) developers perceive that GenAI can help them experience more focus and experience fewer flow disruptions; (3) GenAI can help developers pursue more meaningful conversations with their colleagues by shifting human interaction towards clarification, joint reasoning, and exploring alternative perspectives; and (4) in the presence of GenAI, developers report still seeking human-to-human interaction for contextual expertise, mentorship, and social connection. Together, these findings showwhat changes,when it changes, andwhat teams can do nextin response to this shift in team interaction dynamics, where GenAI increasingly handles routine technical queries and human conversations center on context, reasoning, and connection. Teams can adopt norms for delegating questions, sustain human judgment in complex decisions, and create space for expertise, mentorship, and connection, alongside increasing technical self-sufficiency.

Software development, often perceived as a technical endeavor, is fundamentally a social activity requiring collaboration among team members. Acknowledging this, the software development community has devised strategies to address possible collaboration-related shortcomings. Various studies have attempted to capture the social dynamics within software engineering. These studies developed methods to identify numerous teamwork issues and proposed various approaches to address them. However, there is a need for a comprehensive bottom-up exploration from practitioner’s perceptions to common patterns. This article introduces the concept of undesirable patterns in collective development, referring to potential teamwork problems that may escalate if unaddressed. Through 38 in-depth exploratory interviews, we identify and classify 42 patterns, revealing their origins and consequences. To the best of our knowledge, some patterns, like Teamwork pipeline bottleneck , were never reported before. Subsequent surveys, 436 and 968 participants each, explore the significance and frequency of the undesirable patterns and evaluate potential tools and features to manage these patterns. The study contributes a nuanced understanding of undesirable patterns, evaluating their impact and proposing pragmatic tools and features for industrial application. The findings provide a valuable foundation for further in-depth studies and the development of tools to enhance collaborative software engineering practices.

Collaboration platforms, such as GitHub and Slack, are a vital instrument in the day-to-day routine of software engineering teams. The data stored in these platforms has a significant value for data-driven methods that assist with decision-making and help improve software quality. However, the distribution of this data across different platforms leads to the fact that combining it is a very time-consuming process. Most existing algorithms for socio-technical assistance, such as recommendation systems, are based only on data directly related to the purpose of the algorithms, often originating from a single system.