Classic fault localization techniques can automatically provide information about the suspicious code blocks that are likely responsible for observed failures. This information is useful, but not sufficient to completely understand the causes of failing executions, which still require further (time-consuming) investigations to be exactly identified. A useful and comprehensive source of information is frequently given by the set of unexpected events that have been observed during failures. Sequences of unexpected events are usually simple to be interpret, and testers can guess the expected correct sequences of events from the faulty sequences. In this paper, we present a tool that automatically identifies anomalous events that likely caused failures, filters the possible false positives, and presents the resulting data by building views that show chains of cause-effect relations, i.e., views that show when anomalous events are caused by other anomalous events. The use of the technique to investigate a fault in the Tomcat application server is also presented in the paper. © 2009 IEEE.
Mariani, L., Pastore, F., & Pezzè, M. (2009). A toolset for automated failure analysis. In Proceedings of the 2009 IEEE 31st International Conference on Software Engineering (ICSE) (pp.563-566). Washington : IEEE Computer Society.
|Citazione:||Mariani, L., Pastore, F., & Pezzè, M. (2009). A toolset for automated failure analysis. In Proceedings of the 2009 IEEE 31st International Conference on Software Engineering (ICSE) (pp.563-566). Washington : IEEE Computer Society.|
|Carattere della pubblicazione:||Scientifica|
|Presenza di un coautore afferente ad Istituzioni straniere:||No|
|Titolo:||A toolset for automated failure analysis|
|Autori:||Mariani, L; Pastore, F; Pezzè, M|
|Data di pubblicazione:||2009|
|Nome del convegno:||International Conference on Software Engineering - formal demo|
|Appare nelle tipologie:||02 - Intervento a convegno|