Feature: Automotive
Figure 2: Runtime trace showing the sequence of events that led to an intermittent watchdog reset
progressively starved under fi xed-priority scheduling, the queue backed up, the send blocked, and the watchdog was never serviced. T e fi x was a task priority adjustment. Aſt er reordering the
priorities so that ControlTask ran above ServerTask, the watchdog margin became a fl at line at a constant 10ms, and the problem didn’t recur. What is instructive here is not the specifi c fault – priority
inversions and queue saturation are well-understood problems – but the diagnostic path. Without runtime visibility, the engineer had a reset and a hypothesis space that included hardware faults, soſt ware bugs and confi guration errors. With Tracealyzer, the entire causal chain from priority imbalance to queue saturation to blocked send to missed watchdog service was visible in the trace data, navigable interactively and resolved in a single instrumentation pass. For teams developing safety-critical systems, this kind of evidence – a documented, reproducible account of system temporal behaviour and its causal chain – also has value beyond debugging, contributing to the body of verifi cation artefacts that safety cases depend on.
From debugging to observability T e growing importance of runtime visibility is contributing to a broader shiſt in engineering practice. Historically, diagnostic tools were introduced reactively when a problem needed investigation. Increasingly, organisations are building observability into the development process itself, ensuring that runtime information is available throughout the soſt ware lifecycle – an approach described as Observability Driven Development. T e same information that helps diagnose a fault also supports performance optimisation, integration testing and regression
32 October 2026
www.electronicsworld.co.uk
analysis. During development it surfaces issues before they become diffi cult-to-reproduce fi eld problems. During system integration it provides objective insight into soſt ware behaviour under realistic workloads. Following deployment, it enables teams to investigate failures using data captured from real operating conditions rather than reconstructed in a laboratory.
An engineering requirement for the SDV era As vehicle architectures continue their transition towards soſt ware-defi ned platforms, the challenge facing soſt ware teams is becoming less about implementing individual functions and more about managing the interactions between them. T e systems being developed today are larger, more interconnected and more dynamic than those of previous generations and must support continuous soſt ware evolution while maintaining the reliability, safety and performance that automotive products demand. Meeting these demands requires more than additional testing.
It needs a clearer understanding of how soſt ware actually behaves during execution – understanding that neither static analysis nor conventional instrumentation readily provides. Runtime observability doesn’t replace established development
and validation practices. Rather, it provides insight into aspects of system behaviour that are otherwise diffi cult to access: the actual scheduling decisions made by the RTOS, the real timing margins of safety-critical functions, the communication patterns between concurrent tasks under load, and so on. By making these interactions visible, it enables engineers to diagnose complex faults more effi ciently, build verifi cation evidence with greater confi dence and maintain genuine control over increasingly sophisticated soſt ware platforms.
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