ABSTRACT Developers utilize issue tracking systems to track ideas, feedback, tasks, and bugs for projects in the open‐source software ecosystem of GitHub. In this context, extensive bug reports and feature requests are raised as issues that need to be resolved. This makes issue resolution prediction become more and more important in project management. To address this problem, this paper constructed a multiple feature set from the perspectives of project, issue, and developer, by combining static and dynamic features of issues. Then, we refine a feature set based on the feature's importance. Furthermore, we proposed a method to explore what features and how these features affect the prediction of issue resolution time. Experiments are conducted on a dataset of 46,735 resolved issues from 18 popular GitHub projects to validate the effectiveness of the refined feature set. The results show that our prediction method outperforms the baseline methods.

Abstract The phenomenon of crossover cooperation and convergence among services has gained increasing attention in the modern service industry. Service boundaries have been expansively stretched into other domains rather than limited to their original domains to achieve value creation, fostering the emergence of crossover services. Consequently, a complex service ecosystem takes shape. However, there is a lack of the convergence‐evolution mechanism of crossover services for the adaptive transformation of service providers' businesses in this context. To address this problem, this paper proposes population‐based and community‐based convergence‐evolution patterns from the ecological perspective. Based on the analysis of these evolution patterns and the driven force of service evolution, we propose an ecology‐oriented evolution analysis method. Furthermore, we devise an automated tool to support the evolution design of crossover service ecosystems. Case studies and evaluation experiments show the feasibility and effectiveness of our proposed method and the corresponding tool.