Tool Speeds Automotive Trace Timing Analysis By 10x

timing analysis

To accelerate the development of autonomous vehicles, Luxoft has launched LuxTrace, a new and improved web-based version of TraceAnalyzer 4.0, a timing analysis tool for visualizing system timing in terms of the constituent ECUs, controllers, processors, buses, and networks. Luxoft says the new version can process large traces up to ten times faster than the previous version.

As LuxTrace is the modern web-based solution of the company’s desktop application TraceAnalyzer, it can be integrated into continuous workflow.

LuxTrace now enables engineers to formulate all timing requirements using a flexible test-case centric representation. Being Python-based, the tool is said to allow users to test even the most complex timing requirements automatically. Also, easy templates are provided for general test cases and a central, database-backed repository for all the tracing data is provided.


The tool also allows all test-cases to be run fully automatically, with test reports available interactively in the browser or in user-specific formats like Excel.

LuxTrace can be configured centrally on the server and can be operated by multiple users at the same time. This allows all timing projects to be reviewed in a single place by everyone on the team.

“With autonomous cars around the corner, the need for more accurate and faster real-time software testing has never been more critical,” said Dr. Marek Jersak, Director of Autonomous Drive at Luxoft. “Here at Luxoft, we’ve invested in providing software-based solutions that accelerate the development of next-generation vehicles, whilst ensuring safety and security. That’s why we’ve revolutionised the TraceAnalyzer tool by developing LuxTrace.”

Dr. Jersak continued: “For an engineer, understanding system timing and performance is fundamental to designing and verifying real-time systems. It is now easier to specify timing requirements, while the accuracy of timing measurements has also improved. The ability to perform analyses ten times faster will accelerate the development of driverless projects and ultimately help develop smoother driving experiences.”

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