Log-Based Reduction by Rewriting

Software systems often produce logs which contain
information about the execution of the systems. When an error
occurs, the log file with the error is reported for subsequent
analysis. The longer the log file, the harder to identify the
cause of the observed error. This problem can be considerably
simplified, if we reduce the log length, e.g., by removing
events which do not contribute towards finding the error. This
paper addresses the problem of log reduction by rewriting
the reported log in such a way that it preserves the ability
to reproduce the same error. The approach exploits rewrite
rules inferred from a set of predefined algebraic rewrite
rule patterns, capturing such properties as commutativity and
identity. The paper presents an algorithm for inferencing the
rewrite rules from logs and a terminating reduction strategy
based on these rules. Being log-based the inference algorithm
is inherently imprecise. So the inferred rules need to be
inspected by an expert before actually being used for rewriting.
The approach is language independent and highly flexible.
The paper formally defines all used concepts and discusses a
prototype implementation of a log reduction framework. The
prototype was empirically validated on a web shop application.