Fault localization, i.e., locating faults in code, such as
faulty statements or expressions, which are responsible for observed
failures, is traditionally a manual, laborious, and tedious task.
Recent years have seen much progress in automated techniques for fault
localization. A particularly promising approach is to utilize program
execution spectra to analyze passing and failing runs and
compute how likely each statement is to be faulty. Techniques based
on this approach have so far largely focused on either using
statistical analysis or similarity-based measures, which have a
natural application in evaluating such runs. However, in spite of some
initial success, the current techniques lack the effectiveness of
localizing the faults with a high degree of confidence in real
applications.
Our thesis is that information theoretic feature selection can
provide a basis for novel techniques that mix coverage of
different program elements for improving the effectiveness of fault
localization using program spectra. Our basic insight is that each
additional failing or passing run can increase the information
diversity with respect to the program elements, which can help
localize faults in code. For example, the statements with maximum
feature diversity information can point to the most suspicious lines
of code. This dissertation presents a new fault localization approach
that embodies our insight and introduces Bernoulli divergence
for feature selection and uses it as the foundation for two novel
techniques: (1) mixing of branch and statement coverage information;
and (2) varying of feature granularity from function-level to
statement-level. An experimental evaluation using a suite of subject
programs commonly used in evaluation of fault localization techniques
shows that our approach provides an effective basis for fault
localization. / text
| Identifer | oai:union.ndltd.org:UTEXAS/oai:repositories.lib.utexas.edu:2152/23208 |
| Date | 18 February 2014 |
| Creators | Roychowdhury, Shounak |
| Source Sets | University of Texas |
| Language | en_US |
| Detected Language | English |
| Format | application/pdf |
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