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Modern software systems become too complex to be tested and validated.Detecting software partial failures in complex systems at runtime assist tohandle software unintended behaviors, avoiding catastrophic software failuresand improving software runtime availability. These detection techniques aim tofind the manifestation of faults before they finally lead to unavoidablefailures, thus supporting following runtime fault tolerant techniques. Wereview the state of the art articles and find that the content failures accountfor the majority of all kinds of software failures, but its detection methodsare rarely studied. In this work, we propose a novel failure detectionindicator based on the software runtime dynamic execution information forsoftware content failures. The runtime information is recorded during softwareexecution, then transformed to a measure named runtime entropy and finally fedinto machine learning models. The machine learning models are built to classifythe intended and unintended behaviors of the objected software systems. Aseries of controlled experiments on several open source projects are conductedto prove the feasibility of the method. We also evaluate the accuracy ofmachine learning models built in this work.

Detection Software Content Failures Using Dynamic Execution Information