A Recovery-Oriented Approach for Software Fault Diagnosis in Complex Critical Systems

This paper proposes an approach to software faults diagnosis in complex fault tolerant systems, encompassing the phases of error detection, fault location, and system recovery. Errors are detected in the first phase, exploiting the operating system support. Faults are identified during the location phase, adopting on a machine learning approach; this phase then triggers the proper recovery action for the occurred fault - actuated in the third phase. Feedback actions are also adopted in the location phase to improve detection quality over time. A real world application from the Air Traffic Control field has been used as case study for evaluating the proposed approach. Experimental results, achieved by means of fault injection, show that the diagnosis engine is able to diagnose faults with high accuracy and at a low overhead.