This note explains why scientific-computing habits are useful to this repository's review style. It is a working-method note, not a domain identity claim and not a reason to expand repository scope.
Scientific-computing work rewards runs that can be repeated from explicit inputs. That habit maps directly to this repository's strongest reviewer surface:
- checked-in fixtures instead of private source material
- deterministic commands that can be rerun locally
- generated artifacts that can be compared against known outputs
- documentation that separates what was run from what was inferred
For sbom-diff-and-risk, this means example reports, policy sidecars, SARIF
samples, and release evidence should stay reproducible from public-safe inputs.
The point is not to claim broad expertise; it is to make review evidence easier
to repeat.
Scientific-computing workflows often make the pipeline visible: ingest, normalize, transform, summarize, and report. That pattern helps keep this repository inspectable.
For the flagship SBOM tool, the useful pipeline boundary is:
- parse SBOMs or dependency manifests
- normalize package records into a stable internal shape
- compute local diffs and heuristic findings
- apply explicit local policy when requested
- emit machine-readable and human-readable review artifacts
Each stage should have a clear input and output. When a later report includes context from an earlier stage, the report should preserve enough provenance for a reviewer to understand where the value came from. Hidden enrichment, opaque scoring, and untraceable conclusions work against that goal.
Scientific-computing review also depends on knowing what the data cannot prove. That habit matters here because dependency evidence is easy to overstate.
The repository should keep uncertainty boundaries explicit:
- local manifests and SBOMs prove what they contain, not what the ecosystem currently knows
- optional enrichment is evidence for that run, not a universal truth source
- policy output is a local decision, not a package safety verdict
- missing evidence should stay visible as missing evidence
- unknowns should be reported as unknown or
not_evaluated, not filled with guesses
This is why the docs keep non-claims close to the examples. A reviewer should be able to say what was observed, what was reproduced, and what remains outside the evidence boundary.
Use scientific-computing background as a discipline for reproducible evidence,
clear data flow, and careful uncertainty handling. Do not use it as a reason to
add unrelated project surfaces, broaden claims, or dilute the flagship
sbom-diff-and-risk reviewer route.