一句话：以包导入的非典型组合度量 Python 项目的创新性，发现创新度越高 star 越多，但团队更小且更易被弃养。
论文将创新理解为对已有知识的重新组合，把软件创新操作化为项目对既有库的非典型组合，即从 import 语句中提取的包依赖组合的新颖程度。作者对 Python 开源生态进行了大规模定量研究，考察项目创新性与 GitHub star 数、贡献者团队规模及长期被弃养风险之间的关系。结果显示：创新性越高的项目越受欢迎（star 更多）；但在控制项目规模后，这些项目往往由更小的团队贡献，并且在长期更可能被放弃维护。作者据此指出，创新与开源可持续性密切相关，且在某种程度上相互对立。
Who creates the most innovative open-source software projects? And what fate do these projects tend to have? Building on a long history of research to understand innovation in business and other domains, as well as recent advances towards modeling innovation in scientific research from the science of science field, in this paper we adopt the analogy of innovation as emerging from the novel recombination of existing bits of knowledge. As such, we consider as innovative the software projects that recombine existing software libraries in novel ways, i.e., those built on top of atypical combinations of packages as extracted from import statements. We then report on a large-scale quantitative study of innovation in the Python open-source software ecosystem. Our results show that higher levels of innovativeness are statistically associated with higher GitHub star counts, i.e., novelty begets popularity. At the same time, we find that controlling for project size, the more innovative projects tend to involve smaller teams of contributors, as well as be at higher risk of becoming abandoned in the long term. We conclude that innovation and open source sustainability are closely related and, to some extent, antagonistic.
@inproceedings{paperbot11332,
title = {Novelty Begets Popularity, But Curbs Participation - A Macroscopic View of the Python Open-Source Ecosystem},
author = {Hongbo Fang and James Herbsleb and Bogdan Vasilescu},
booktitle = {ICSE},
year = {2024},
doi = {10.1145/3597503.3608142}
}