Guided Algebraic Specification Mining for Failure Simplification

Software systems often produce logs that capture information about their execution behaviour. 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, exhibiting such properties as commutativity and identity. The paper presents an algorithm for rewrite rules inference, 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 a human 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.