Dr. Mark Padgham

Open-source communities have been disappearing for several years. AI accelerates that by encouraging working in isolation. For open-source software to survive, we need to re-connect communities. I describe the principles of my "askfirst" software which enables developers to broadcast to AI-coding systems that they should be directly contacted by users of those systems to help improve their code. Tools like these will play an important role in ensuring the ongoing sustainability of open-source communities.

CRAN represents and curates a complex software ecosystem of over 25,000 packages. This ecosystem constantly evolves as packages are submitted, updated, and archived. We analysed the development of CRAN over its entire lifetime, both in terms of package inter-dependencies and the internal structures of every version of every package. These analyses used the "pkgstats" tool for static code analysis, developed by rOpenSci, to analyse the statistical properties of every version of every CRAN package. Statistics measured by "pkgstats" include metadata on authorship and licenses; message translations; numbers of files, functions, lines of code, and lines of documentation; analyses of function calls both within a package and to functions defined in external packages; package dependency networks; and many other aspects. All of these statistics were analysed both over the entirety of CRAN on every day of its evolution, and on every package across sequential releases. Results reveal a contrast between the collective evolution of CRAN, and the evolution of individual packages. The CRAN ecosystem is becoming less stable because of an increasing dominance of new packages, yet this is counter-balanced by an increase in stability through individual packages becoming more mature. Balancing these confounding effects emerges as a key aspect of maintaining the stability of the complex software ecosystem of CRAN.

Cities are complex evolving systems. This talk outlines future visions for spatial analysis software in which data on relationships have equal primacy to data on objects. The talk weaves as example narrative and shows how software I have co-developed can be used to explore evolving relationships alongside spatial structures.

Research software is commonly developed by an internal "core" set of people, and often funded by grants with defined end dates. Research software which survives often does so through being adopted and maintained by broader communities beyond that initial core. Community engagement is thus vital to the success of research software. rOpenSci is an organization dedicated to "Transforming science through open data, software & reproducibility," and does so through actively nurturing a large and diverse community of research software authors and users. One of our primary activities is peer review for software in the R computer language, which has been running for almost 10 years. A recent large, multi-year project expanded the scope of peer review to include statistical software, the primary focus of the R language itself. This project was led and coordinated by our own core set of rOpenSci people, resulting in a comprehensive system for documenting and assessing statistical software. As with research software, we will best ensure the long-term survival and relevance of our system through fostering a transition towards community-led maintenance. This talk will describe challenges and successes in our efforts to engage and expand our community to support our statistical software system, and will draw important parallels with the abiding importance of community engagement in research software development.