Lightweight Defect Localization for Java

Abstract

Abstract.A common method to localize defects is to compare the coverage of passing and failing program runs: A method executed only in failing runs, for instance, is likely to point to the defect. Some failures, though, come to be only through a specific sequence of method calls, such as multiple deallocation of the same resource. Such sequences can be collected from arbitrary Java programs at low cost; comparing object-specific sequences predicts defects better than simply comparing coverage. In a controlled experiment, our technique pinpointed the defective class in 39% of all test runs.

Runtime Verification in Context: Can Optimizing Error Detection Improve Fault Diagnosis?

“…This technique records information about the order in which system components are executed during a run of the system. Intuitively, this technique should provide richer information when compared with spectra, which are order-independent, and the study presented in [27] confirms this intuition at least with respect to hit spectra.…”

“…Sequence-based fault localization [27]. This technique records information about the order in which system components are executed during a run of the system.…”

“…Consequently, the approach of Dallmeier et al [27] uses several techniques to reduce the cost of capturing and storing sequence information. The most aggressive technique stores all sub-sequences of component invocations of length k. This results in the recording of a sequence set for each program run.…”

“…In general, we expect that these two information sources may overlap, Σ ∩D = ∅. For example, the set of observations for error detection of typestate properties are exactly what are needed to form sequence sets for the fault localization in [27]. While it may be permissible for optimization to degrade D in some way without impacting the output of fault diagnosis, we define a more conservative property here that suffices as long as fault diagnosis is deterministic relative to its input.…”

Fault localization in embedded software based on a single cyclic trace

“…• Tarantula's, the original coefficient used in [8] to assist fault localization using a visualization technique; • The Jaccard index, a well-known statistic measure used to compare sets and used in the Pinpoint framework [24]; • AMPLE, the coefficient used in the tool AMPLE (Analyzing Method Patterns to Locate Errors) to locate error in object-oriented software [25]; • Ochiai, a coefficient originally used in molecular biology, but used successfully in fault localization [1];…”