In 2020 or so, ACM swapped its definitions of reproducibility and replicability to be more in line with the broader social sciences community. I see the reasoning for the swap, but I don't think that the new definition is quite right either. The crux of the issue is that definitions both communities are using are a shorthand that doesn't map appropriately across communities.
Last week, Kaleigh Clary presented her work, Stick it to the Man: Correcting for Non-Cooperative Behavior of Subjects in Experiments on Social Networks at USENIX Security. One of the things I really like about this work is the focus on analysis and implications of non-cooporative actors in network experiments from a PL/systems perspective.
Here in the MaPLE lab, we treat the techniques students cultivate in their programming systems classes as general methods for answering a broad range of research questions that arise in and around software when it is used in nontraditional ways. As a result, students without formal training in the techniques we use often ask: "What is PL?," whereas those with training often ask: "How is what you do PL?"
Everyone should have an elevator pitch about their research. When we start out, that pitch is very focused on a particular project, problem, or technique. As time passes, we form a more expansive research vision that can encompass new domains, related problems, and complementary techniques, but still ties everything together in a coherent philosophy, with a long-term goal. My pitch is: programming languages (PL) and software engineering (SE) form the methodological foundation for next-generation advancements in data-driven scientific inquiry. Just as scientific instruments made new experiments and discoveries possible, so too will new programming languages, systems, frameworks, and platforms.