Trace timing analysis accelerates in the automotive sector

27th March 2018
Lanna Cooper


Luxoft Holding has launched LuxTrace, a new and improved web-based version of TraceAnalyzer 4.0, a timing analysis tool for visualising system timing in terms of the constituent ECUs, controllers, processors, buses and networks, which processes large traces up to ten times faster than the previous version.

With LuxTrace, Luxoft has turned the desktop application TraceAnalyzer into a modern web-based solution. As such, it is ready for deployment in a continuous integration workflow. LuxTrace now enables engineers to formulate all timing requirements using a flexible test-case centric representation.

Being Python-based, users can test even the most complex timing requirements automatically. At the same time, easy templates are provided for general test cases and offers a central, database-backed repository for all your tracing data.

In addition, all test-cases can be run fully automatically, with test reports available interactively in the browser or in user-specific formats like Excel. LuxTrace offers a central configuration on the server and can be operated by multiple users at the same time. This means all timing projects can 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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