Introducing DevAlert Sandbox

“In a world that is increasingly defined by software, OEMs making intelligent devices need an automatic feedback loop from device to developer to enable rapid detection and debugging of software anomalies, both during development and in deployment. By including Percepio DevAlert in shipped devices, OEMs can react 100x faster on software issues in the field and provide rapid updates to safeguard customer operations. DevAlert Sandbox provides an easy route to exploring the concept in general, and DevAlert in particular,” said Percepio CEO and Founder Johan Kraft.

Percepio DevAlert is a cloud-connected monitoring framework for anomaly detection and remote debugging of RTOS-based embedded software, during development and in deployment at scale. DevAlert enables a diagnostic feedback loop from deployed devices back to OEMs and operators, and provides immediate and full insight into anomalies in the device software via software tracing and the integrated Tracealyser tool. This way, OEMs can provide rapid solutions to safeguard their devices and related business, and reduce debugging and customer support costs. DevAlert is designed to fit in small microcontroller-based devices and scales to large device fleets without causing alert fatigue.

DevAlert Sandbox lets users experience a full DevAlert setup directly in their web browser using a simulated device, and without having to write any code. A ready-to-run application example is included with source code and development tools, allowing developers to explore and experiment with use-cases of software anomaly monitoring, and expand the solutions to their own projects.

The example application provided by DevAlert Sandbox is an elevator control system running on a virtual Arm microcontroller, powered by the xPack QEMU Arm simulator and monitored using Percepio DevAlert. A graphical simulator interface allows for triggering anomalies in the elevator controller, which are detected and reported using Percepio DevAlert together with software traces that explain the reported anomalies.