8 research outputs found
Test Case Purification for Improving Fault Localization
Finding and fixing bugs are time-consuming activities in software
development. Spectrum-based fault localization aims to identify the faulty
position in source code based on the execution trace of test cases. Failing
test cases and their assertions form test oracles for the failing behavior of
the system under analysis. In this paper, we propose a novel concept of
spectrum driven test case purification for improving fault localization. The
goal of test case purification is to separate existing test cases into small
fractions (called purified test cases) and to enhance the test oracles to
further localize faults. Combining with an original fault localization
technique (e.g., Tarantula), test case purification results in better ranking
the program statements. Our experiments on 1800 faults in six open-source Java
programs show that test case purification can effectively improve existing
fault localization techniques
Diversity Maximization Speedup for Localizing Faults in Single-Fault and Multi-Fault Programs
Advance online 06/09/2014</p
Causality-Guided Adaptive Interventional Debugging
Runtime nondeterminism is a fact of life in modern database applications.
Previous research has shown that nondeterminism can cause applications to
intermittently crash, become unresponsive, or experience data corruption. We
propose Adaptive Interventional Debugging (AID) for debugging such intermittent
failures. AID combines existing statistical debugging, causal analysis, fault
injection, and group testing techniques in a novel way to (1) pinpoint the root
cause of an application's intermittent failure and (2) generate an explanation
of how the root cause triggers the failure. AID works by first identifying a
set of runtime behaviors (called predicates) that are strongly correlated to
the failure. It then utilizes temporal properties of the predicates to
(over)-approximate their causal relationships. Finally, it uses fault injection
to execute a sequence of interventions on the predicates and discover their
true causal relationships. This enables AID to identify the true root cause and
its causal relationship to the failure. We theoretically analyze how fast AID
can converge to the identification. We evaluate AID with six real-world
applications that intermittently fail under specific inputs. In each case, AID
was able to identify the root cause and explain how the root cause triggered
the failure, much faster than group testing and more precisely than statistical
debugging. We also evaluate AID with many synthetically generated applications
with known root causes and confirm that the benefits also hold for them.Comment: Technical report of AID (SIGMOD 2020