Generating TypeScript Bindings for Verification Tools (RocqStat, VectorCAST)

Ship safer dashboards: Type-safe bindings for RocqStat and VectorCAST outputs

Hook: If you're responsible for bringing safety-critical verification data into dashboards, alerts, or CI gates, you've felt the pain of brittle parsers, unreliable alerts, and missing types. In 2026 the stakes are higher: Vector's acquisition of RocqStat and the push to unify timing analysis with VectorCAST mean more teams will need type-safe wrappers that turn verification logs and reports into reliable telemetry.

Why this matters now (2026)

In January 2026 Vector announced it had acquired RocqStat to integrate worst-case execution time (WCET) and timing analysis into the VectorCAST toolchain. This consolidation accelerates tool output standardization but increases the velocity and volume of verification outputs flowing through observability backends and dashboards.

That trend creates both opportunity and risk. Opportunity: a unified source of truth for timing + functional verification that can be consumed programmatically. Risk: if you treat tool outputs as opaque text, alerts and dashboards become noisy and false-positive prone. The fix is to build type-safe wrappers and small code generators that convert logs and reports into structural TypeScript types and runtime validators.

What you'll get from this guide

• Concrete patterns to parse RocqStat / VectorCAST outputs into TypeScript types
• Example generators (ts-morph + zod) that emit types and parsers from samples
• Integration patterns for dashboards, alerts, and CI
• Best practices for publishing typings and keeping bindings maintainable

High-level approach

Turn tool artifacts (XML/JSON/CSV/logs) into:

1. Stable TypeScript type definitions (.d.ts or generated .ts)
2. Runtime validators/parsers (zod, io-ts, or runtype)
3. Transformation layer to observability formats (OTLP, Prometheus metrics, events)
4. CI hooks to fail builds on contract drift

Example: RocqStat JSON -> typed domain model

Assume RocqStat can emit a JSON WCET report (many modern analyzers provide JSON or XML exports). We'll parse a representative JSON into a TypeScript type and runtime validator using zod, then show a small codegen that creates a .ts model for use in your dashboard backend.

Sample RocqStat JSON (simplified)

// rocq-report.json
  {
    "analysisId": "rs-2026-01-14-1234",
    "target": "engine_control",
    "timestamp": "2026-01-14T12:30:00Z",
    "functions": [
      {"name": "calcTorque", "wcet_us": 420, "confidence": 0.98},
      {"name": "updateSensors", "wcet_us": 320, "confidence": 0.95}
    ],
    "globalWcet_us": 1500
  }
  

Write a zod schema and generated TypeScript interface

Use zod for runtime validation and leverage its .infer for static typing.

import { z } from 'zod';

const RocqFunctionSchema = z.object({
  name: z.string(),
  wcet_us: z.number().nonnegative(),
  confidence: z.number().min(0).max(1)
});

const RocqReportSchema = z.object({
  analysisId: z.string(),
  target: z.string(),
  timestamp: z.string().refine(s => !Number.isNaN(Date.parse(s)), { message: 'Invalid ISO date' }),
  functions: z.array(RocqFunctionSchema),
  globalWcet_us: z.number().nonnegative()
});

export type RocqFunction = z.infer;
export type RocqReport = z.infer;

export function parseRocqReport(raw: unknown): RocqReport {
  return RocqReportSchema.parse(raw);
}

Actionable tip

Validate reports at the CI edge (e.g., as a GitHub Action step) and emit a normalized artifact for downstream consumers to avoid repeated parsing logic in dashboards.

Example: VectorCAST XML -> TypeScript generator

VectorCAST historically emits XML reports for tests and coverage. Let's build a tiny generator that reads a sample XML test-report, infers types, and emits typed models using ts-morph. This generator is best run in CI when new samples are added or tooling updates.

Approach

1. Parse XML to a JS object (fast-xml-parser)
2. Normalize field names and pick types (string/number/boolean/array)
3. Emit TypeScript interfaces and zod validators with ts-morph

Minimal code sketch

import { parse } from 'fast-xml-parser';
import { Project, SourceFile } from 'ts-morph';
import fs from 'fs';

function inferType(value: any) {
  if (Array.isArray(value)) return { kind: 'array', item: inferType(value[0]) };
  if (value === 'true' || value === 'false') return { kind: 'boolean' };
  if (!isNaN(Number(value))) return { kind: 'number' };
  if (typeof value === 'object') return { kind: 'object' };
  return { kind: 'string' };
}

export function generateFromXmlSample(xmlPath: string, outPath: string) {
  const xml = fs.readFileSync(xmlPath, 'utf-8');
  const obj = parse(xml, { ignoreAttributes: false, parseAttributeValue: false });

  // For brevity: assume top-level "TestReport" structure
  const root = obj.TestReport;

  const project = new Project();
  const file = project.createSourceFile(outPath, '', { overwrite: true });

  // Infer simple TestCase type
  const testCaseExample = root.TestCases.TestCase[0];
  const props = Object.keys(testCaseExample).map(k => ({ name: k, type: inferType(testCaseExample[k]) }));

  // Emit interface
  file.addInterface({
    name: 'VectorTestCase',
    isExported: true,
    properties: props.map(p => ({ name: p.name, type: mapToTs(p.type) }))
  });

  project.saveSync();
}

function mapToTs(t: any): string {
  if (t.kind === 'array') return `${mapToTs(t.item)}[]`;
  if (t.kind === 'number') return 'number';
  if (t.kind === 'boolean') return 'boolean';
  if (t.kind === 'object') return 'Record';
  return 'string';
}

Note: This is intentionally minimal. Production generators should sample multiple reports, unify naming, and allow overrides via a config file.

Design patterns for robust bindings

• Separation of concerns: generators create static types and validators; parsers use validators; dashboard code consumes the typed model only.
• One canonical artifact: store the validated JSON or a normalized protobuf in your artifact store; downstream consumers read it rather than raw logs.
• Schema evolution strategy: version your report contract (e.g., analysisV1, analysisV2) and support both in a migration layer.
• Fail early in CI: add unit tests that assert schema validity against example files shipped with the repo and integrate with your CI pipeline.

Observability: mapping types to metrics and alerts

Once you have a typed model, mapping to observability systems becomes predictable and testable.

Example metric mapping

From our RocqReport we can emit Prometheus-style metrics or OTLP spans/metrics. Example metrics:

• rocq_wcet_us{function,analysisId,target}
• rocq_wcet_confidence{function} (gauge 0..1)
• rocq_global_wcet_us{analysisId,target}

// pseudo-code
import { parseRocqReport } from './rocq-model';
import { MeterProvider } from '@opentelemetry/sdk-metrics';

const meter = new MeterProvider().getMeter('rocq');
const wcetGauge = meter.createObservableGauge('rocq_wcet_us');

function publish(reportRaw: unknown) {
  const report = parseRocqReport(reportRaw);
  report.functions.forEach(f => {
    // push gauge with labels
  });
}

Alerting rules

Because types guarantee fields exist and have expected types, alert rules are simpler and more precise:

• Alert if rocq_global_wcet_us > threshold for target
• Alert if any function wcet_us increased by > X% vs baseline
• Alert if confidence < 0.9 for a critical function

Automation: codegen, CI, and contracts

Automate generator runs in CI. Recommended flow:

1. Tooling update or new sample report triggers generator
2. Generator emits types and validators into a generated folder
3. Run unit tests to ensure parsing passes
4. Publish a versioned artifact (npm package or internal package) with types

Add a contract test that runs nightly comparing latest tool outputs against expected shapes and flags schema drift. Integrate contract tests with your observability and alerting systems so schema drift raises tickets automatically.

Packaging and distribution

For cross-team reuse, publish your bindings one of two ways:

• Internal npm package (e.g., @acme/rocq-bindings) with runtime validators and TS types
• Contribute typings to DefinitelyTyped if the tool has a community edition but no typings; maintainers often accept typings for parsers or SDK wrappers

Include README examples, sample reports, and CI contract tests in the package to build trust across teams.

Testing strategies

• Fuzz sample ingestion: mutate fields, remove keys to confirm validators correctly reject invalid inputs.
• Golden files: keep small canonical reports and assert round-trip parse -> normalize -> serialize remains stable.
• Type-level tests: use tsd (type assertions) to ensure exported types match expectations for public APIs.

Advanced patterns: hybrid codegen and manual overrides

Tool outputs often include footguns—string codes, optional fields, or nested polymorphism. A practical compromise is to codegen the bulk of types and keep a small hand-maintained "overrides" file for domain-specific adjustments.

// generated/rocq-types.ts (auto)
export interface GeneratedFunction { name: string; wcet_us: number | string; confidence?: number }

// overrides.ts (manual)
export interface RocqFunction extends GeneratedFunction { wcet_us: number } // normalize

Then ensure tests validate that overrides are consistent with raw input via converters that coerce types safely.

Practical checklist before shipping

• Do you validate raw artifacts at CI ingress?
• Are types and validators generated, versioned, and tested?
• Do you publish bindings for internal reuse?
• Have you mapped types to observability (metrics, logs, traces)?
• Is there a schema-evolution and deprecation plan?

Case study: small team integrating RocqStat into a dashboard (fictional, realistic)

A small embedded team at a Tier-1 supplier integrated RocqStat reports into their verification dashboard in 6 weeks:

1. Week 1: Gather sample JSON/XML reports from RocqStat and VectorCAST exports
2. Week 2: Prototype parser with zod to validate and normalize
3. Week 3: Build generator that outputs types + validators and commit generated artifacts
4. Week 4: Map fields to Prometheus metrics and Grafana dashboard panels
5. Week 5: Add CI contract tests to fail on schema drift
6. Week 6: Publish internal npm package and integrate with upstream CI

Result: the dashboard stopped producing false-positive WCET alerts and engineers had a single source for verification telemetry.

Future predictions and trends (2026 and beyond)

• Unified tool outputs: With Vector integrating RocqStat, expect more standardized exports across timing and testing tools—good news for codegen.
• Schema-first verification: Teams will adopt schema contracts for verification artifacts and validate in CI, similar to API contract testing.
• Runtime typed telemetry: Observability platforms will natively accept typed verification events (OTLP with schema hints), making typed bindings easier to integrate.
• AI-assisted mapping: Tools that suggest type mappings from samples will mature, but expert review remains crucial for safety-critical semantics.

Pitfalls to avoid

• Avoid brittle string parsing; prefer structured exports (JSON/XML) or ask tool providers to enable them.
• Don't rely solely on static types; add runtime validators for safety-critical flows.
• Avoid ad-hoc parsers duplicated across services — centralize bindings to a single package.
• Watch for silent schema changes when tools update; enforce CI checks and version your schema.

"Typing verification outputs is the difference between a dashboard you trust and one you ignore." — Experienced verification engineer

Quick starter templates

Create two npm packages in your mono-repo:

1. bindings/rocq — generated types + zod validators + parser utility
2. bindings/vectorcast — XML parser + generator + TS models

Each package should export:

• parseReport(raw: unknown): StronglyTypedReport
• normalizeReport(report: StronglyTypedReport): NormalizedArtifact
• metricsPublisher(normalized): void

Publishing to DefinitelyTyped?

If your bindings are broadly reusable and the tool has an open community, consider submitting typings to DefinitelyTyped. For internal or proprietary flows, publish an internal scoped package. In both cases, maintain examples and sample artifacts to build trust.

Wrap-up: actionable next steps

1. Inventory your verification artifacts (RocqStat, VectorCAST, others) and collect 5–10 representative samples.
2. Prototype runtime validators with zod or io-ts for core report types.
3. Build a small generator (ts-morph) to emit .ts models and validators and add it to CI.
4. Publish the bindings internally and replace ad-hoc parsers with the new package.
5. Map key fields to metrics and add alerting rules that rely on typed fields.

Final thoughts

As tooling consolidates — such as Vector’s 2026 acquisition of RocqStat — your ability to quickly create reliable, typed bindings will pay off in fewer false alarms and faster debugging loops. Treat verification outputs like any other contract: validate them, type them, and automate their ingestion.

Call to action

If you'd like a starter repo with the generator, zod schemas, and CI contract tests tailored to your VectorCAST or RocqStat outputs, get in touch or clone the example kit (link in the repo). Start by collecting sample reports this week and run a prototype generator in CI — your dashboard will thank you.

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