Engineering Standards for Scientific Software
Why this matters
Most of the software I build is internal, domain-heavy, and used by teams who are not primarily software engineers. In that environment, “the code runs” is a low bar. The real question is whether the product can be trusted, maintained, and handed over without turning into folklore.
The details vary by client and domain, but my standards are fairly stable.
1. Start with the operating problem
I try to understand the real workflow before I touch architecture:
- who uses the product
- what decision or operation it supports
- what failure would actually cost
- what has to be auditable, reproducible, or explainable
This sounds obvious, but it prevents a lot of over-engineering. Scientific software often fails because it optimizes the elegance of the implementation over the friction of the user workflow.
2. Prefer explicit architecture to accidental structure
Whether the tool is an R Shiny application, an analysis pipeline, or an ML-assisted workflow, I want the structure to be legible:
- clear boundaries between UI, business logic, and data access
- naming that still makes sense six months later
- configuration handled deliberately rather than scattered through scripts
- deployment assumptions stated explicitly
I am not attached to complexity for its own sake. The right architecture is the lightest one that still remains maintainable under change.
3. Tests and checks should reduce anxiety
I do not treat testing as a compliance ritual. The point is to make change safer.
Depending on the project, that usually means some mix of:
- automated tests for the logic that can realistically break
- input validation and constrained state transitions
- regression checks on critical outputs
- smoke checks around deployment paths
In regulated or high-consequence contexts, I also care about the evidence around the system: what was validated, what assumptions were made, and what would need re-checking after a change.
4. Documentation is not optional overhead
The documentation I value most is the kind that makes a system survivable:
- short architecture notes
- operational runbooks
- deployment instructions
- onboarding material for the next developer or analyst
- user-facing guides when the audience needs them
If the only reliable documentation is “ask the person who built it”, the product is not done.
5. CI/CD and reproducibility are part of product quality
I work best when environments and delivery are explicit:
- version control as the source of truth
- reproducible environments with
renv,uv, containers, or pinned tooling when appropriate - CI/CD to reduce manual drift
- deployments that are boring on purpose
In scientific environments, reproducibility is not just engineering hygiene. It is often the difference between a result being usable and a result being untrustworthy.
6. Handover and training are part of delivery
I like projects where the output is not only software, but increased capability in the team using it.
That can take several forms:
- mentoring a junior developer or analyst
- running internal upskilling sessions
- pairing on difficult debugging or design decisions
- turning repeated explanations into reusable templates or guides
I do not see knowledge transfer as a nice extra. It is one of the ways you make a product durable.
7. Remote collaboration needs explicit habits
I am comfortable working remotely, but good remote work is not passive. It usually relies on:
- clear written decisions
- visible priorities
- early screen-sharing when something is ambiguous
- concise feedback loops
- enough documentation that async work remains aligned
This is one reason I value writing: it helps teams coordinate without relying on memory.
What this looks like in practice
Across projects, these standards usually show up as some combination of:
- R Shiny applications with modular structure and deployment discipline
- reusable R packages or reporting templates
- qualification-minded internal tooling in regulated environments
- GitHub Actions or equivalent CI/CD for repeatable delivery
- architecture and handover material that support maintenance
The exact stack changes. The underlying principles do not change much.
The short version
The kind of software I want to build is:
- useful to the people doing the work
- understandable by the people maintaining it
- explicit about trade-offs
- documented well enough to survive handover
- rigorous enough to be trusted in scientific settings
That is a more durable goal than chasing novelty for its own sake.