Research
We're a new lab. One study is running now; more will follow. This page will grow as that work does.
What we're interested in
Our territory is the lived experience of software engineering — the texture of writing and reading code, the small negotiations of working on a team, the way a craft reshapes itself under new tools and new pressures. We sit closer to qualitative software research and human factors than to benchmarks or developer productivity metrics, and we think that's where the most interesting questions about developer experience still live.
We're drawn to questions that don't have numerical answers. Questions like:
- How do engineers come to feel ownership over code an agent wrote — to trust it enough to ship it, and to call it yours when it breaks?
- When a team feels like it's working well, what are the people inside it noticing?
- How are software engineers making sense of LLM-based tools — as collaborators, as threats, as something else entirely?
- What does it feel like when the craft shifts underneath you — and how do you decide what's worth learning?
- What makes software work feel meaningful over a long career?
Current study
Running now
Adapting to LLM-induced change in software engineering
Since LLM-based programming tools arrived, the practice of software engineering has been moving faster than the consensus about how it should be done. Tools, workflows, and ideas about what "good" looks like are all in flux. Engineers are left to either ignore the shift and risk being left behind, or to find their own way through it — learning, experimenting, resisting, reinventing — often without much to go on.
We're looking at how practitioners are actually living through that change. We're keeping the specific questions and framing off this page on purpose, so that people who take part come in with their own experience rather than ours.
If any of this resonates, we'd love to hear from you. Read about participating →
Methods
We work qualitatively: long interviews, careful transcription, and grounded-theory-inflected analysis that lets patterns emerge from what people actually tell us. We prefer small-N and deep over large-N and shallow. Most of our time is spent re-reading and re-thinking rather than collecting.
Past work
We haven't published anything as LESE Lab yet — the lab is new. Both of us have prior academic publication histories from before the lab; you can find those on the about page.