Automated Derivation of Application-Specific Error Detectors Using Dynamic Analysis

Abstract

This paper proposes a novel technique for preventing a wide range of data errors from corrupting the execution of applications. The proposed technique enables automated derivation of fine-grained, application-specific error detectors based on dynamic traces of application execution. The technique derives a set of error detectors using rule-based templates to maximize the error detection coverage for the application. A probability model is developed to guide the choice of the templates and their parameters for error-detection. The paper also presents an automatic framework for synthesizing the set of detectors in hardware to enable low-overhead, runtime checking of the application. The coverage of the derived detectors is evaluated using fault-injection experiments, while the performance and area overheads of the detectors are evaluated by synthesizing them on reconfigurable hardware.