Data mining approach to temporal debugging of embedded streaming applications

One of the greatest challenges in the embedded systems area is to empower software developers with tools that speed up the debugging of QoS properties in applications. Typical streaming applications, such as multimedia (audio/video) decoding, fulfill the QoS properties by respecting the real-time deadlines. A perfectly functional application, when missing these deadlines, may lead to cracks in the sound or perceptible artifacts in the image. We start from the premise that most of the streaming applications that run on embedded systems can be expressed under a data ow model of computation, where the application is represented as a directed graph of the data flowing through computational units called actors. It has been shown that in order to meet real-time constraints the actors should be scheduled in a periodic manner. We exploit this property to propose SATM - a novel approach based on data mining techniques that automatically analyzes execution traces of streaming applications, and discovers significant breaks in the periodicity of actors, as well as potential causes of these breaks. We show on a real use case that our debugging approach can uncover important defects and pinpoint their location to the application developer.