Requirements-Driven Root Cause Analysis Using Markov Logic Networks

Root cause analysis for software systems is a challenging diagnostic task, due to the complexity emanating from the interactions between system components and the sheer size of logged data. This diagnostic task is usually assisted by human experts who create mental models of the system-at-hand, in order to generate hypotheses and conduct the analysis. In this paper, we propose a root cause analysis framework based on requirement goal models. We consequently use these models to generate a Markov Logic Network that serves as a diagnostic knowledge repository. The network can be trained and used to provide inferences as to why and how a particular failure observation may be explained by collected logged data. The proposed framework improves over existing approaches by handling uncertainty in observations, using natively generated log data, and by providing ranked diagnoses. The framework is illustrated using a test environment based on commercial off-the-shelf software components.