Dynamic analysis techniques have been extensively adopted to discover causes of observed failures. In particular, anomaly detection techniques can infer behavioral models from observed legal executions and compare failing executions with the inferred models to automatically identify the likely anomalous events that caused observed failures.

Unfortunately the output of these techniques is limited to a set of independent suspicious anomalous events that does not capture the structure and the rationale of the differences between the correct and the failing executions. Thus, testers spend a relevant amount of time and effort to investigate executions and interpret these differences, reducing effectiveness of anomaly detection techniques.

Automata Violations Analyzer, AVA, is a technique to automatically produce candidate interpretations of software failures from anomalies identified by anomaly detection techniques. Interpretations capture the rationale of the differences between legal and failing executions with user understandable patterns that simplify identification of failure causes. Empirical validation conducted both with synthetic cases and third-party systems showed that AVA produces useful interpretations.

A detailed description of AVA is provided in the following paper:

Anton Babenko, Leonardo Mariani, and Fabrizio Pastore. Ava: automated interpretation of dynamically detected anomalies. 
In ISSTA �09: Proceedings of the eighteenth international sym- posium on Software testing and analysis, pages 237�248, 
New York, NY, USA, 2009. ACM.