Fix Sldv Incompatibility

SLDV Incompatibility Fixer

Fix Simulink Design Verifier incompatibilities by diagnosing the model and applying the matching fix. Block-level fixes go through model_edit; solver/config-level fixes are the one exception that requires setting the parameter directly (see Step 3).

When to Use

• User asks "why is my model incompatible with SLDV?"
• User asks "make this model compatible with SLDV"
• User asks to fix SLDV incompatibility issues
• User runs sldvcompat and gets false
• A test-generation or design-error-detection run errors out on an unsupported construct, and the user asks to get past it — make the model analyzable first, then hand back to that workflow

When NOT to Use

• The model is already compatible (sldvcompat returns true) — there is nothing to fix.
• The task is to run an SLDV analysis mode (test generation, design error detection, property proving) on an already-compatible model — use the corresponding analysis skill, not this one.
• The incompatibility is intended and the user wants to keep the unsupported construct — do not silently rewrite the model.
• The request is a general Simulink modeling edit unrelated to SLDV compatibility.

Scope

The core job is diagnose → fix → confirm compatible (Steps 1–4). That is the default endpoint. Running an SLDV analysis mode (test generation, design error detection) and verifying behavior on the original model is optional and only happens when the surrounding intent calls for it (Step 5). A user who just wants compatibility gets the fix and nothing more.

Prerequisites

• Products: Simulink and Simulink Design Verifier, R2023b or later.
• The model must build in a normal Simulink compile, with all its external dependencies present. SLDV compatibility is a separate question from whether the model compiles at all. Two non-compat cases also make sldvcompat return false: a genuine authoring bug (undefined block parameter, unresolved reference), fixed by repairing the model; and a genuinely missing external dependency — a data-dictionary file, referenced-model file, data file, library, or workspace variable the model needs that is not present / cannot be located — fixed by providing the resource, not by editing the model (see Step 1.5). Neither is fixed with a compatibility rule. (A resource that is present but merely opaque to SLDV — a protected .slxp reference, external custom code, an S-function — is different again: that you do fix, by stubbing.) When a model fails sldvcompat, Step 1 also runs a normal-mode compile probe and reports result.compiles / result.buildError so you can tell these apart before matching a rule.
• This skill's functions live in its scripts/ directory. Call them with evaluate_matlab_code using project_path set to that folder so MATLAB can find them.

Workflow

Step 1: Run the Diagnostic Tool

Call via evaluate_matlab_code:

matlabCopy code
% project_path = the skill's scripts/ folder (via evaluate_matlab_code)
result = sldv_diagnose_incompatibility('<model_path>');

This will:

• Create a copy named <model>_sldv_compatible.slx in the same folder
• Run sldvcompat on the copy

It returns a result struct with these top-level fields:

• .compatible — true/false, the sldvcompat status on the copy
• .copyPath — full path to the working copy (<model>_sldv_compatible.slx). Later steps pass this to model_edit and to sldv_verify_consistency
• .copyName — name of the copy with no extension (use with sldvcompat / save_system / close_system)
• .findings — struct array, one entry per issue sldvcompat itself reports, each with .msgid, .message, .type, .blockPath, and .blockType
• .compiles — logical, populated only when .compatible == false: the result of a normal-mode Simulink compile probe on the copy. It is a diagnostic aid, not a gate — the findings above stay authoritative and are matched against the catalog regardless. Its purpose is the case where the findings are only generic: true means the model builds (a real SLDV limitation), false means it does not compile at all (a plain authoring bug the catalog does not cover — see Prerequisites; fix the model first).
• .buildError — when .compiles == false, the compile error's .identifier and .message; empty strings otherwise.

sldvcompat is authoritative: each finding already names the offending object. For a block-level issue, .blockPath is the full block path (nesting included, e.g. mdl/Sub/White Noise) and .blockType its BlockType. For a config-level issue (variable-step solver, nonempty InitFcn, concurrent execution, …), .blockPath is the model name, .blockType is empty, and .msgid identifies the setting.

The diagnostic does not scan blocks or pre-read config params: use model_overview / model_read / model_query_params to inspect whatever a finding points to, and the rule catalog in references/incompatibility-rules.md to decide the fix.

A config-level finding can MASK block-level findings. A variable-step solver, for example, stops sldvcompat before it reaches the blocks. So more issues may surface only after you fix the config-level ones and re-run the diagnostic — never assume the first pass lists everything (Step 4 loops).

If result.compatible == true, the model is already compatible. Done.

Step 1.5: Triage a Missing External Dependency (ask, don't fix)

Some incompatibilities are not something to fix in the model at all — the model is fine, but an external resource it depends on is genuinely absent from the environment: a data dictionary (.sldd) file that is not on disk, a referenced model file that cannot be located, a From File / From Spreadsheet data file that does not exist, a library / MATLAB class / workspace variable a parameter resolves to that is nowhere on the path. SLDV cannot analyze what it cannot find, so sldvcompat fails — but applying a compatibility rule (replacing or stubbing the block) would be wrong: it hides a real gap and discards the model's intended behavior.

Recognize this class before matching a rule: it shows up as result.compiles == false together with a result.buildError (or a result.findings entry) whose message says a named resource is missing / not found / does not exist / cannot be located or resolved. The identifiers vary by resource and release (e.g. a missing data-dictionary file, a not-found referenced-model file, an absent data file), so match on the meaning — a named external artifact that isn't there — not on a fixed list of msgids. See "Missing External Dependencies" in references/incompatibility-rules.md.

This is NOT the present-but-opaque case — do not confuse the two. A resource that exists but SLDV cannot see inside is a normal stubbing job, not a missing dependency: a protected (.slxp) referenced model, a Model block referencing one that is present, external custom code / an S-function whose source is opaque, a MATLAB Function block calling an external routine. These are on disk and resolve fine — SLDV just can't analyze their internals. Fix them (stub the block), do not ask the user for them. If result.compiles == true (the model builds), it is by definition not a missing dependency — go straight to Step 2. Only defer to the user when the named artifact truly cannot be found.

When you hit one, do not fix or rewrite the model. Instead:

1. Explain why it is incompatible, in plain terms, quoting the exact resource named in result.buildError.message / the finding message (e.g. "the model references data dictionary params.sldd, which SLDV cannot find on the path").
2. Ask the user to provide it — supply the missing file / dictionary / referenced model / variable, or point the skill at its location so it is resolvable — and then re-run the diagnostic.

Only once the dependency is resolvable does the normal diagnose → match → fix flow apply. Two other compiles == false cases are not this and must not be routed here: a plain authoring bug (undefined parameter, broken expression) is resolved by repairing the model; a present-but-opaque resource (protected .slxp reference, external custom code, S-function) is resolved by stubbing the block per Step 2/3. Ask the user only when the named artifact genuinely cannot be found — never for a resource that is present but unanalyzable.

Step 2: Match the Rule

The rule catalog is not inlined here — it lives in references/incompatibility-rules.md. Load and follow that file. For each entry in result.findings, match .msgid (and, for block-level findings, the .blockType at .blockPath) against the catalog, then apply the listed fix. Use model_read on .blockPath to inspect the offending block's parameters. Use the catalog's Disambiguation Guide when a msgid maps to multiple rules, and its Stubbing Strategy / Integrator port-matching / block-reduction sections for the cross-cutting guidance the fixes reference.

Step 2.5: Localize a Generic Finding (only when the finding is not actionable)

Most findings already name the offending object in .blockPath. But some are generic — the message is vague (e.g. a bare SLDV:Compatibility:Generic) and .blockPath points at a container (the model root or a SubSystem) rather than a leaf block. A generic, container-scoped finding does not tell you what to fix. When (and only when) you hit one, narrow it:

matlabCopy code
% project_path = the skill's scripts/ folder
loc = sldv_localize_incompatibility('<model_or_atomic_subsystem_path>');

This drills the hierarchy, scoping sldvcompat onto each atomic subsystem, atomic Stateflow subchart, and referenced model, and keeps descending into whichever children are individually incompatible. It descends through virtual subsystems (which sldvcompat cannot scope) to the next atomic boundary. It returns:

• loc.culprits — one entry per deepest responsible scope (the narrowest subsystem/subchart/model that is incompatible while none of its scopable children are), each with that scope's .path, .kind, and .findings.
• loc.drilled — true if the search narrowed below the scope you passed in.
• loc.searched — every scope it scoped sldvcompat onto (for transparency).

Then match the rule (Step 2) against each culprit's .findings, which are now scoped to the responsible subsystem instead of the whole model. If loc.culprits still points at a scope whose own findings are generic (the issue is that scope's wiring/config, not a nested block), inspect that scope directly with model_read / model_query_params.

Do not run localization for findings that already name a leaf block — use those directly. Localization is only for the generic, container-scoped case.

Step 3: Apply the Fix

Based on the matched rule:

• For block-level fixes: use model_edit on result.copyName. This is the default — all block edits (replacement, stubbing, port matching, rewiring) go through model_edit.
• For configuration / model-root fixes: these change solver/config settings (variable-step solver, InitFcn, concurrent execution, …), which model_edit cannot touch — it only operates on blocks. This is the one exception where you set the parameter directly via evaluate_matlab_code (set_param on the model root, e.g. the solver type). Most such fixes are the config-level rules (Rules 1–7), but a handful of otherwise block-level rules also include a config-level set_param step (e.g. clearing CustomSourceCode, aligning referenced-model hardware settings, forcing a fixed-step solver, or disabling BlockReduction) — those config steps use set_param too. The exception is defined by the kind of fix (a config/model-root parameter), not by the rule number. Do not generalize it to block-level edits: never reach for set_param to add, delete, replace, or rewire a block — that is always model_edit's job.

Most block-level rules do semantic replacement (swap the unsupported block for an analyzable equivalent). When no faithful equivalent exists — or the block's internals are irrelevant to the analysis — use the general stubbing strategy instead (see "Stubbing Strategy" in references/incompatibility-rules.md). Stubbing abstracts a block's output to a free value so SLDV stops analyzing inside it; prefer a bounded stub so analysis stays realistic.

Before any block replacement, guard against optimized-out blocks. Block reduction can delete blocks (bus selectors/creators, IC blocks, …) before the replacement runs, so it silently finds nothing to replace — check get_param(result.copyName,'BlockReduction') and disable it first if needed. See "Block Reduction / Optimized-Out Blocks" in references/incompatibility-rules.md for the procedure and its open caveats.

Step 4: Verify and Finish (default endpoint)

Run the diagnostic again:

matlabCopy code
[compat, ~] = sldvcompat(result.copyName);

If still incompatible, repeat Steps 2–3 (multiple issues may exist).

Once compatible, save and report — this is where the skill stops by default:

matlabCopy code
save_system(result.copyName);
close_system(result.copyName);

Report the compatible copy path to the user. If the user only asked to make the model compatible, you are done. Do not run test generation or design error detection unless the surrounding task calls for it (see Step 5).

Step 5: Offer a Behavior Consistency Check (optional — only when the intent is present)

Step 4 is the default endpoint. Compatibility does not confirm the fix preserved behavior. When — and only when — the surrounding intent calls for behavior preservation (test generation, design error detection, or the user explicitly asks), an optional back-to-back consistency replay (sldv_verify_consistency) can raise confidence the fix preserved behavior by replaying SLDV-generated tests through the original model.

This is a specialized sub-case with its own API, status codes, and eligibility rules. Load and follow references/consistency-check.md for the full procedure. Do not load it for a plain compatibility request — stop after Step 4.