Software control flow error detection and correlation with system performance deviation

Detecting runtime errors helps avoid the cost of failures and enables systems to perform corrective actions prior to failure occurrences. Control flow errors are major impairments of system dependability during component interactions. Existing control flow monitors are susceptible to false negatives due to possible inaccuracies of the underlying control flow representations. Moreover, avoiding performance overhead and program modifications are major challenges in these monitoring techniques. In this paper, we construct a connection-based signature approach for detecting errors among component interactions. We analyze the monitored system performance and examine the relationship of the captured error state parameters with the system performance deviation. Using the  PostgreSQL  8.4.4 open-source database system with randomly injected errors, the experimental evaluation results show a decrease in false negatives using our approach relative to the existing techniques. It also demonstrates a significant ability of identifying the responsible components and error state patterns for system performance deviation.