Data-Augmented Software Diagnosis

Amir Elmishali, Roni Stern, Meir Kalech

AAAI 2016: 4003-4009

Software fault prediction algorithms predict which software
components is likely to contain faults using machine learning
techniques. Software diagnosis algorithm identify the faulty
software components that caused a failure using model-based
or spectrum based approaches. We show how software fault
prediction algorithms can be used to improve software diagnosis.
The resulting data-augmented diagnosis algorithm
overcomes key problems in software diagnosis algorithms:
ranking diagnoses and distinguishing between diagnoses with
high probability and low probability. We demonstrate the ef-
ficiency of the proposed approach empirically on three open
sources domains, showing significant increase in accuracy of
diagnosis and efficiency of troubleshooting. These encouraging
results suggests broader use of data-driven methods to
complement and improve existing model-based methods.