A toolset for automated failure analysis

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.

[1]  William G. Griswold,et al.  Dynamically discovering likely program invariants to support program evolution , 1999, Proceedings of the 1999 International Conference on Software Engineering (IEEE Cat. No.99CB37002).

[2]  Steven P. Reiss,et al.  Fault localization with nearest neighbor queries , 2003, 18th IEEE International Conference on Automated Software Engineering, 2003. Proceedings..

[3]  Andreas Zeller,et al.  Lightweight Defect Localization for Java , 2005, ECOOP.

[4]  Leonardo Mariani,et al.  Dynamic Analysis for Integration Faults Localization , 2008 .

[5]  Leonardo Mariani,et al.  Dynamic Detection of COTS Component Incompatibility , 2007, IEEE Software.

[6]  J. William Ahwood,et al.  CLASSIFICATION , 1931, Foundations of Familiar Language.

[7]  M. Lam,et al.  Tracking down software bugs using automatic anomaly detection , 2002, Proceedings of the 24th International Conference on Software Engineering. ICSE 2002.

[8]  James R. Larus,et al.  Mining specifications , 2002, POPL '02.

[9]  Mary Shaw,et al.  Semantic anomaly detection in online data sources , 2002, ICSE '02.

[10]  James R. Larus,et al.  Exploiting hardware performance counters with flow and context sensitive profiling , 1997, PLDI '97.

[11]  David Leon,et al.  An Empirical Study of Test Case Filtering Techniques Based on Exercising Information Flows , 2007, IEEE Transactions on Software Engineering.

[12]  John T. Stasko,et al.  Visualization of test information to assist fault localization , 2002, ICSE '02.

[13]  Andreas Zeller,et al.  Why Programs Fail: A Guide to Systematic Debugging , 2005 .