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Abstract. 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.

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This paper introduces a new type of automated testing oracle, called the execution equivalence (EE) invariants. These invariants can be mined from application logs that capture both application events and application states. The EEinvariants express an equivalence relation on the sequences of application events in terms of equality of respective initial and final states, which these sequences leave in the logs during the run-time. We claim that even equivalences up to a length of four events already provide useful testing oracle. We extended our tool LOPI (LOg-based Pattern Inferencer) with the algorithm for mining EE-invariants, and evaluated the effectiveness of these invariants on a case-study -the web application Flex Store. The evaluation is carried out based on two parameters: the false positive rate and the fault finding capability. Moreover, we compared the strength of LOPI's execution equivalences with Daikon's data invariants. This comparison has shown that Daikon was slightly more effective than LOPI in testing Flex Store. However, we have found a suitable confidence level for LOPI which allows to outperform Daikon.