Knowledge transfer is fundamental to human collaboration and is therefore common in software engineering. Pair programming is a prominent instance. With the rise of AI coding assistants, developers now not only work with human partners but also, as some claim, with AI pair programmers. Although studies confirm knowledge transfer during human pair programming, its effectiveness with AI coding assistants remains uncertain.To analyze knowledge transfer in both human–human and human–AI settings, we conducted an empirical study where developer pairs solved a programming task without AI support, while a separate group of individual developers completed the same task using the AI coding assistant GitHub Copilot. We extended an existing knowledge transfer framework and employed a semi-automated evaluation pipeline to assess differences in knowledge transfer episodes across both settings. We found a similar frequency of successful knowledge transfer episodes and overlapping topical categories across both settings. Two of our key findings are that developers tend to accept GitHub Copilot’s suggestions with less scrutiny than those from human pair programming partners, but also that GitHub Copilot can subtly remind developers of important code details they might otherwise overlook.

Communication among software developers plays an essential role in open-source software (OSS) projects. Not unexpectedly, previous studies have shown that the conversational tone and, in particular, aggressiveness influence the participation of developers in OSS projects. Therefore, we aimed at studying aggressive communication behavior on the Linux Kernel Mailing List (LKML), which is known for aggressive e-mails of some of its contributors. To that aim, we attempted to assess the extent of aggressiveness of 720 e-mails from the LKML with a human annotation study, involving multiple annotators, to select a suitable sentiment analysis tool. The results of our annotation study revealed that there is substantial disagreement, even among humans, which uncovers a deeper methodological challenge of studying aggressiveness in the software-engineering domain. Adjusting our focus, we dug deeper and investigated why the agreement among humans is generally low, based on manual investigations of ambiguously rated e-mails. Our results illustrate that human perception is individual and context dependent, especially when it comes to technical content. Thus, when identifying aggressiveness in software-engineering texts, it is not sufficient to rely on aggregated measures of human annotations. Hence, sentiment analysis tools specifically trained on human-annotated data do not necessarily match human perception of aggressiveness, and corresponding results need to be taken with a grain of salt. By reporting our results and experience, we aim at confirming and raising additional awareness of this methodological challenge when studying aggressiveness (and sentiment, in general) in the software-engineering domain.