Many long-running computer systems record events as they execute, resulting in a dynamic record of system behavior. In large systems, the... Show moreMany long-running computer systems record events as they execute, resulting in a dynamic record of system behavior. In large systems, the event trace may contain thousands of entries and when faced with a problem for analysis, programmers must sort through many disparate events to find those that are related to the system behavior under study and eliminate those that are not. In this research we investigated automatic reduction of event traces to reduce the volume of events and assist in analysis of behavior of large systems. Our approach was to adapt the techniques used in program slicing to compute event trace slices as a means of reduction. Two methods for slicing of event traces were proposed and investigated. The Event Dependence Based method (EDB) uses information available in the event trace to identify dependencies between events and to compute an event trace slice that meets a slicing criterion. The Model Dependence Based method (MDB) incorporates the use of an executable state-based system model to achieve further reduction of traces. The method identifies model-based dependences in the trace to compute trace slices. An experimental study was performed on simulated systems, representative of state-based software systems present in industry to analyze and compare the EDB and MDB slicing methods. Both methods provided significant reduction of event traces, particularly for systems with a low degree of sharing and interaction among resources. However, the MDB method significantly outperformed the EDB method for systems with a high degree of resource sharing. Ph.D. in Computer Science, May 2012 Show less
