| Attribute | Detail |
|---|---|
| Format | ODX (Open Diagnostic data eXchange, ISO 22901-1); or proprietary CANdela format (.cdd) |
| Content | All DIDs (read/write, data type, scaling, session/security), DTCs (code, status, snapshot), routines, memory regions |
| Users | ECU developer (creates spec), tester tool (imports for automation), workshop tool (imports for diagnosis) |
| Workflow | CDD authored in ECU design → imported to CANoe/Python test scripts → same CDD imported to workshop tester (e.g., CANdela Studio → ODXF → Delphi DiagProg) |
| Standard | ISO 22901-1:2008 ODX; ISO 22901-2:2011 OTX (test procedures) |
CDD: Diagnostic Description File Overview
DID Specification in CDD (ODX Format)
XMLdid_spec.odx
Engine Oil Viscosity Grade
0
15
0 1
1
0 0
5 5
10 10
15 15
CDD Validation Against ECU Behaviour
| Validation Step | Tool/Method | What to Catch |
|---|---|---|
| CDD vs ARXML consistency | Script: compare DID list in CDD vs DCM ARXML | DIDs in CDD not implemented in ECU; DIDs in ECU not documented in CDD |
| Session/security requirement | Test matrix script: attempt each DID in each session × level | Access restrictions inconsistent between CDD and ECU |
| Data length | CDD-declared length vs actual ECU response | Wrong DID length causes incorrect scaling in all tools that use CDD |
| Scaling/encoding | CDD physical formula vs actual ECU encoding | 1°C offset in temperature scaling; wrong byte order |
| DTC list completeness | Read 0x19 0x0A (all supported DTCs); compare to CDD DTC list | DTCs stored in ECU but not in CDD = invisible to workshop tools |
Summary
The CDD is the diagnostic contract between ECU developer and tool builder — every DID, DTC, and routine that exists in the ECU must be documented in the CDD with correct length, scaling, session, and security requirements. An incorrect DID length in the CDD causes every tool that imports it to misparse the data silently (scaling appears correct numerically but with wrong physical meaning). CDD validation should be automated: a test script that compares CDD declarations against actual ECU responses for every DID catches the majority of specification-implementation mismatches before workshop tools are delivered.
🔬 Deep Dive — Core Concepts Expanded
This section builds on the foundational concepts covered above with additional technical depth, edge cases, and configuration nuances that separate competent engineers from experts. When working on production ECU projects, the details covered here are the ones most commonly responsible for integration delays and late-phase defects.
Key principles to reinforce:
- Configuration over coding: In AUTOSAR and automotive middleware environments, correctness is largely determined by ARXML configuration, not application code. A correctly implemented algorithm can produce wrong results due to a single misconfigured parameter.
- Traceability as a first-class concern: Every configuration decision should be traceable to a requirement, safety goal, or architecture decision. Undocumented configuration choices are a common source of regression defects when ECUs are updated.
- Cross-module dependencies: In tightly integrated automotive software stacks, changing one module's configuration often requires corresponding updates in dependent modules. Always perform a dependency impact analysis before submitting configuration changes.
🏭 How This Topic Appears in Production Projects
- Project integration phase: The concepts covered in this lesson are most commonly encountered during ECU integration testing — when multiple software components from different teams are combined for the first time. Issues that were invisible in unit tests frequently surface at this stage.
- Supplier/OEM interface: This is a topic that frequently appears in technical discussions between Tier-1 ECU suppliers and OEM system integrators. Engineers who can speak fluently about these details earn credibility and are often brought into critical design review meetings.
- Automotive tool ecosystem: Vector CANoe/CANalyzer, dSPACE tools, and ETAS INCA are the standard tools used to validate and measure the correct behaviour of the systems described in this lesson. Familiarity with these tools alongside the conceptual knowledge dramatically accelerates debugging in real projects.
⚠️ Common Mistakes and How to Avoid Them
- Assuming default configuration is correct: Automotive software tools ship with default configurations that are designed to compile and link, not to meet project-specific requirements. Every configuration parameter needs to be consciously set. 'It compiled' is not the same as 'it is correctly configured'.
- Skipping documentation of configuration rationale: In a 3-year ECU project with team turnover, undocumented configuration choices become tribal knowledge that disappears when engineers leave. Document why a parameter is set to a specific value, not just what it is set to.
- Testing only the happy path: Automotive ECUs must behave correctly under fault conditions, voltage variations, and communication errors. Always test the error handling paths as rigorously as the nominal operation. Many production escapes originate in untested error branches.
- Version mismatches between teams: In a multi-team project, the BSW team, SWC team, and system integration team may use different versions of the same ARXML file. Version management of all ARXML files in a shared repository is mandatory, not optional.
📊 Industry Note
Engineers who master both the theoretical concepts and the practical toolchain skills covered in this course are among the most sought-after professionals in the automotive software industry. The combination of AUTOSAR standards knowledge, safety engineering understanding, and hands-on configuration experience commands premium salaries at OEMs and Tier-1 suppliers globally.