Scope of work
AI Regression, Test Orchestration
Industry
ADAS & SDV
Platform
Distributed / Multi-ECU
Testing
OTA, Fault Injection
TRAXIS™ is SpeedEDGE’s AI-enhanced evolution of integration testing — purpose-built for complex automotive stacks. It unifies multi-ECU orchestration, AI-generated test synthesis, safety-aware regression, and OTA validation into a single, CI/CD-native engine. TRAXIS accelerates releases while raising confidence, delivering risk-based coverage with explainable analytics.
AI-Generated Test Synthesis: Automatically proposes new integration tests from commit diffs, interface changes, and past failures. Uses model-based reasoning to synthesize boundary and fault-path scenarios, reducing human effort while expanding coverage.
Coordinate distributed scenarios across ECUs, HIL rigs, and virtual targets. TRAXIS packs and schedules suites to minimize wall-clock time, auto-resumes flaky jobs, and parallelizes safe paths for faster signal-to-confidence.
Inject network jitter, bus errors, sensor dropouts, clock skew, CPU/memory pressure, and storage faults. Validate graceful degradation and recovery with trace-linked verdicts and replayable seeds.
Pre-flight OTA bundles in a digital-twin loop, verify rollback safety, and run delta-aware regression that focuses on impacted interfaces, contracts, and timing budgets.
Risk-based test selection tied to safety goals (ASIL) and cybersecurity requirements. TRAXIS surfaces gaps, ranks risk, and explains why each test ran — enabling audits and faster sign-off.
First-class integrations with pipelines to trigger, gate, and annotate runs on every change. Artifacts include rich logs, traces, KPIs, and machine-readable reports for continuous compliance.
Run scenarios in simulation + HIL with identical seeds and contracts to reproduce field issues deterministically.
Automated interface contract checks and timing budget monitors with SLA-style thresholds and alerts.
Retry heuristics, flaky-test quarantine, and intelligent reruns that conserve lab time without masking regressions.