Trace Normalization

Identifying truly distinct traces is crucial for the performance of many dynamic analysis activities. For example, given a set of traces associated with a program failure, identifying a subset of unique traces can reduce the debugging effort by producing a smaller set of candidate fault locations. The process of identifying unique traces, however, is subject to the presence of irrelevant variations in the sequence of trace events, which can make a trace appear unique when it is not. In this paper we present an approach to reduce inconsequential and potentially detrimental trace variations. The approach decomposes traces into segments on which irrelevant variations caused by event ordering or repetition can be identified, and then used to normalize the traces in the pool. The approach is investigated on two well-known client dynamic analyses by replicating the conditions under which they were originally assessed, revealing that the clients can deliver more precise results with the normalized traces.