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Existing fault-localization techniques combine various program features and similarity coefficients with the aim of precisely assessing the similarities among the dynamic spectra of these program features to predict the locations of faults. Many such techniques estimate the probability of a particular program feature causing the observed failures. They often ignore the noise introduced by other features...
In continuous integration, a tight integration of test case prioritization techniques and fault-localization techniques may both expose failures faster and locate faults more effectively. Statistical fault-localization techniques use the execution information collected during testing to locate faults. Executing a small fraction of a prioritized test suite reduces the cost of testing, and yet the subsequent...
Fault localization is a major activity in software debugging. Many existing statistical fault localization techniques compare feature spectra of successful and failed runs. Some approaches, such as SOBER, test the similarity of the feature spectra through parametric self-proposed hypothesis testing models. Our finding shows, however, that the assumption on feature spectra forming known distributions...
Predicate-based statistical fault-localization techniques locate fault-relevant predicates in a program by contrasting the statistics of the values of individual predicates between successful and failure-causing runs. While short-circuit evaluations are common in program execution, treating predicates as atomic units ignores this fact, masking out various types of important statistics. On the contrary,...
Pervasive programs should be context-aware, which means that program functions should react according to changing environmental conditions. Slicing, as an important class of code analysis techniques, can clarify the dependence between program artifacts and observable system states to facilitate debugging, testing, and other analyses. Existing program slicing techniques, however, do not take the contextual...
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