Duals in Spectral Fault Localization
Lee Naish
Hua Jie (Jason) Lee
Numerous set similarity metrics have been used for ranking
"suspiciousness"
of code in spectral fault localization, which uses execution profiles
of passed and failed test cases to help locate bugs. Research in data
mining has identified several forms of possibly desirable symmetry
in similarity metrics. Here
we define several forms of "duals" of
metrics, based on these forms of symmetries. Use of these duals, plus
some other slight modifications, leads to several new similarity
metrics.
We show that versions of several previously proposed metrics are
optimal,
or nearly optimal, for locating single bugs. We also show that a form
of
duality exists between locating single bugs and locating
"deterministic"
bugs (execution of which always results in test case failure). Duals of
the various single bug optimal metrics are optimal for locating such
bugs.
This more theoretical work leads to a conjecture about how different
metrics could be chosen for different stages of software development.
Keywords:
debugging, bug localization, program spectra, set similarity
An almost identical version
of this paper will appear in the proceedings of ASWEC 2013
and you will be able to get from IEEE, for a price.
Lee