In this blog post, we’re going to demonstrate how to create LTTng tracepoints and how to use Tracealyzer for Linux to measure certain metrics based on these tracepoints.
Your organization can get to market faster with higher-quality products when given better insight into the “dark side of the code”—the actual behavior of the full software system. Intended and actual behavior may differ in myriad ways that are not apparent from the source code.
The real test of IoT devices comes after shipping, when thousands of end users start using your product, sometimes in unexpected and untested ways. No software is entirely bug free, so some users will run into those remaining bugs. The question is how many and what you do about it.
When developing an application that’s targeting a Linux-based system, it is important to configure our system to maximize performance, because misconfiguration can limit application performance.
One of the biggest problems in embedded development today is understanding what a software stack or demo that you didn’t write is doing. In this post, we’ll examine how we can find this out using the Tracealyzer communication flow diagram.
Quite a few embedded software developers don’t know whether their applications meet their timing requirements. In today’s Tracealyzer Hands On post, we will explore how to use Tracealyzer to verify task timing and scheduling.
Have you ever seen your embedded system behave strangely and had that sinking feeling that you might have a memory leak? Tracealyzer offers several different methods to detect memory leaks.
What if you want to visualize some application data in Tracealyzer, measure the time between two events or monitor a state machine? In this post, we will show how you can set up this kind of custom logging.
In the previous Hands On post we introduced the concept of intervals, which is the time between any two events and can be added to the timeline. In this post, we will take the interval one step further and see how we can use Tracealyzer to monitor state machines.
In this post, we visualise the custom interval and state machine information available in Tracealyzer and explore how we can use it to better understand our application.
Within Tracealyzer’s trace view, tasks, events and state machines are now organized into view fields, collections of tasks, intervals or events. In this post, we are going to examine how you can use those views to simplify working with Tracealyzer.
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