# Quality and Operations

This document describes the engineering standards, quality posture, and operational model of **Radixor**.

It is intentionally broader than a test checklist. The purpose of the project is not only to provide a fast stemmer, but to provide one whose behavior is explainable, measurable, reproducible, and straightforward to audit. That objective influences both the implementation style and the surrounding operational practices.

## Engineering position

Radixor is developed with a strong preference for objective quality signals over informal confidence.

In practical terms, that means the project emphasizes:

- deterministic behavior,
- reproducible compiled artifacts,
- very high structural test coverage,
- very high mutation resistance,
- explicit benchmark methodology,
- minimal operational ambiguity in deployment.

This is not treated as a cosmetic quality layer added after the implementation. It is part of the design goal of the project itself.

## Why quality discipline matters here

A stemmer can appear deceptively simple from the outside. In practice, however, correctness depends on several interacting layers:

- dictionary parsing,
- patch-command generation,
- trie construction,
- reduction semantics,
- binary persistence,
- runtime lookup behavior.

A defect in any one of these layers can produce subtle and difficult-to-detect errors, including silent ranking drift, loss of ambiguity information, reconstruction inconsistencies, or incorrect stemming outcomes under only a narrow subset of inputs.

For that reason, Radixor aims to be validated not only by example-based tests, but by a broader quality model that combines functional testing, mutation testing, coverage analysis, benchmark visibility, and artifact publication.

## Determinism and reproducibility

Determinism is a foundational property of the project.

Given the same dictionary input and the same reduction settings, the project aims to produce:

- the same compiled trie semantics,
- the same local value ordering,
- the same observable `get()` and `getAll()` behavior,
- the same persisted binary output structure in semantic terms.

This matters for more than technical elegance. It enables:

- stable search behavior across deployments,
- reproducible build outputs,
- reliable regression analysis,
- explainable differences when a dictionary or reduction setting changes.

A deterministic system is easier to test, easier to reason about, and safer to integrate into production pipelines.

## Test strategy

The project is intended to maintain very high confidence in both core correctness and behavioral stability.

The recommended execution strategy is defined by the tagged test profiles in [Test taxonomy and execution filtering](test-taxonomy-and-filtering.md). In practice, teams can execute profile tasks directly:

- `./gradlew ciSmoke`: fast local/PR safety checks (`unit`, excluding `slow`; additionally excludes
  `CompileIntegrationTest` as a defensive safeguard).
- `./gradlew ciSlow`: enterprise heavy gate for all tests marked with `slow` (typically
  production dictionary and large corpus verification). This should be used for scheduled/manual
  hardening gates and not in standard release build.
- `./gradlew ciCore`: behavioral coverage of trie and frequency-trie paths (`unit` + `property` where applicable)
- `./gradlew ciIntegration`: pipeline and CLI integration path checks
- `./gradlew ciCompat`: compatibility and regression verification for persisted artifacts
- `./gradlew ciRelease`: full non-slow suite for release-confidence runs (all test tags except `slow`,
  plus explicit name-based exclusion of `CompileIntegrationTest*` and
  `StemmerPatchTrieLoaderTest$BundledDictionaryTests*` as additional guardrails)
- `./gradlew ciNightly`: extended fuzz profile for robustness hardening
- `./gradlew ci`: umbrella profile depending on smoke/core/integration/compat

## Test taxonomy and execution filtering

The full tag taxonomy and executable filter examples are documented in
[Test taxonomy and execution filtering](test-taxonomy-and-filtering.md).

### Structural coverage

High code coverage is treated as a useful signal, but not as a sufficient goal on its own. Coverage is valuable only when the covered scenarios actually pressure the implementation in meaningful ways.

In Radixor, strong coverage is expected across areas such as:

- patch encoding and application,
- mutable trie construction,
- subtree reduction,
- compiled trie lookup,
- binary serialization and deserialization,
- reconstruction from compiled state,
- dictionary parsing and CLI behavior.

### Mutation resistance

Mutation testing is especially important for this project because it helps distinguish superficial test execution from genuinely discriminating tests.

A project can report high line or branch coverage while still failing to detect semantically dangerous implementation drift. Mutation testing provides a stronger objective signal: whether the test suite actually notices meaningful behavioral changes.

For Radixor, very high mutation scores are therefore part of the intended engineering standard, not an optional vanity metric.

### Boundary and negative-path validation

The project also benefits from extensive negative and edge-case testing, for example around:

- malformed patch commands,
- missing or corrupt binary data,
- invalid CLI arguments,
- ambiguous mappings,
- dominance-threshold edge conditions,
- reconstruction of reduced compiled tries,
- empty inputs and short words.

These cases are important because many real integration failures occur at the boundary conditions, not in the central happy path.

## Quality signals and published evidence

The project publishes durable quality artifacts through GitHub Pages so that important signals remain externally inspectable rather than existing only as transient CI output.

Those published surfaces include:

- unit test results,
- coverage reports,
- mutation testing reports,
- static analysis reports,
- benchmark outputs,
- software composition artifacts.

This publication model improves transparency and makes it easier to inspect the project’s quality posture without having to reconstruct the CI environment locally.

## Operational model

Radixor is designed around a clean separation between preparation-time work and runtime execution.

### Preparation phase

Preparation includes:

- creating or refining dictionary data,
- compiling the dictionary into a reduced read-only trie,
- validating the resulting artifact,
- persisting it as a deployable binary stemmer.

### Runtime phase

Runtime usage is intentionally simpler:

- load the compiled artifact,
- reuse the resulting trie,
- perform fast lookups and patch application,
- avoid rebuilding or reparsing during live request handling.

This separation reduces startup unpredictability, keeps runtime behavior stable, and makes deployment artifacts explicit.

## Production posture

For production use, the preferred model is straightforward:

1. prepare or refine the lexical resource,
2. compile it offline,
3. validate the resulting artifact,
4. deploy the compiled binary,
5. load it once and reuse it.

This model has several advantages:

- no runtime compilation cost,
- no repeated parsing overhead,
- clear versioning of stemming behavior,
- better reproducibility across environments,
- simpler operational diagnosis when results change.

## Auditability and dependency posture

Radixor deliberately avoids external runtime dependencies.

That choice serves a practical engineering goal: the project should be easy to audit from both a correctness and a security perspective, without forcing downstream users to reason through a large dependency graph or a complex software supply chain for core functionality.

A dependency-free core does not make a project automatically secure, but it does simplify several important activities:

- source review,
- behavioral auditing,
- release inspection,
- software composition analysis,
- long-term maintenance.

In operational terms, this means there is less hidden behavior outside the project’s own codebase and less need to evaluate third-party runtime libraries for the core implementation path.

## Security-minded operational guidance

The project’s operational simplicity should be preserved in deployment practice.

Recommended principles include:

- treat source dictionaries as controlled inputs,
- generate compiled artifacts in known build environments,
- version compiled artifacts explicitly,
- avoid loading untrusted binary stemmer files,
- keep benchmark, test, and quality outputs attached to the same revision that produced the artifact.

These practices support traceability and reduce ambiguity about what exactly is running in production.

## Performance as a quality concern

Performance is not isolated from quality; for Radixor, it is part of the project’s engineering contract.

The benchmark suite exists to make throughput behavior measurable and historically visible. At the same time, benchmark interpretation must remain disciplined. Absolute numbers can vary by environment, especially when published through shared CI infrastructure. Sustained relative behavior and reproducible local benchmark methodology are more meaningful than one-off raw figures.

This is why benchmarking belongs alongside testing and reporting rather than outside the quality discussion altogether.

## Operational observability

Radixor itself is intentionally small and does not attempt to become an observability framework. Instead, integrations should provide the surrounding operational visibility that production systems require.

Typical integration-level observability includes:

- reporting load failures,
- monitoring startup artifact loading,
- measuring lookup throughput in the host application,
- tracking memory usage of loaded compiled tries,
- optionally sampling ambiguity-heavy cases when `getAll()` is part of the application logic.

The project’s role is to remain deterministic and inspectable enough that such operational signals are meaningful.

## What feedback is most valuable

Feedback is especially valuable when it improves the objectivity or professional rigor of the project.

That includes, for example:

- defects in behavioral correctness,
- weaknesses in reduction semantics or edge-case handling,
- benchmark methodology issues,
- gaps in tests or mutation resistance,
- ambiguities in published reports,
- opportunities to improve auditability, reproducibility, or operational clarity.

Project feedback is most useful when it helps strengthen the project as an implementation that can be trusted, reviewed, and maintained at a professional standard.

## Practical summary

Radixor aims to combine:

- strong algorithmic performance,
- deterministic behavior,
- very high validation standards,
- transparent published quality evidence,
- low operational ambiguity,
- easy auditability of the core implementation.

That combination is central to the identity of the project. The goal is not merely to be fast, but to be fast in a way that remains explainable, testable, reproducible, and professionally defensible.

## Related documentation

- [Benchmarking](benchmarking.md)
- [Reports](reports.md)
- [CLI compilation](cli-compilation.md)
- [Programmatic usage](programmatic-usage.md)
