Developer question and answering communities rely on experts to provide helpful answers. However, these communities face a shortage of experts. To cultivate more experts, the community needs to quantify and analyze the rules of the influence of extrinsic motivations on the ongoing contributions of those developers who can become experts in the future (potential experts). Currently, there is a lack of potential expert‐centred research on community incentives. To address this gap, we propose a motivational impact model with self‐determination theory‐based hypotheses to explore the impact of five extrinsic motivations (badge, status, learning, reputation, and reciprocity) for potential experts. We develop a status‐based timeline partitioning method to count information on the sustained contributions of potential experts from Stack Overflow data and propose a multifactor assessment model to examine the motivational impact model to determine the relationship between potential experts’ extrinsic motivations and sustained contributions. Our results show that (i) badge and reciprocity promote the continuous contributions of potential experts while reputation and status reduce their contributions; (ii) status significantly affects the impact of reciprocity on potential experts’ contributions; (iii) the difference in the influence of extrinsic motivations on potential experts and active developers lies in the influence of reputation, learning, and status and its moderating effect. Based on these findings, we recommend that community managers identify potential experts early and optimize reputation and status incentives to incubate more experts.

Microservice architecture is a dominant architectural style in SaaS industry, which helps to develop a single application as a collection of independent, well-defined, and inter-communicating services. The number of microservice-related questions in Q&Awebsites, such as Stack Overflow, has expanded substantially over the last years. Due to its increasing popularity, it is essential to understand the existing problems that microservice developers face in practices as well as the potential solutions to these problems. Such an investigation of problems and solutions is vital for long-term, impactful, and qualified research and practices in microservice community. Unfortunately, we currently know relatively little about such knowledge. To fill this gap, we conduct a large-scale in-depth empirical study on 17,522 Stack Overflow microservice-related posts. Our analysis leads to the first taxonomy of microservice-related topics based on the software development process. By analyzing the characteristics of the accepted answers, we find that there are fewer experts in the microservice than other domains, and such a phenomenon is most significant with respect to the microservice design phase. Furthermore, we perform manual analysis on 6,013 answers accepted by developers and distill 47 general solution strategies for different microservice-related problems, 22 of which are proposed for the first time. For instance, several problems inherent in the delivery phase can be lessened by referring to external sources like GitHub code examples. Our findings can therefore facilitate research and development on emerging microservice systems.