Mutation-Based Graph Inference for Fault Localization

For each class of pro- gram (e.g., Tcas includes 37 faulty versions), we report the averaged number of positive test cases |T + |, the averaged number of negative test cases |T − |,

Our results show (1) expanding test suites used for fault localization using any of our four test objectives, even when the expansion is small, can significantly improve the accuracy

One of the first and most popular fault localization approach is Tarantula, which was introduced by Jones et al. Tarantula is a statement-based method that uses a

That is, test suites with the higher 10% and higher 5% of coverage attainment for statement, branch and weak mutation in Table III do not exhibit practical improvements on the

Causal MSO Sets Generation for the FCS System Once the set of sensors to achieve maximum diagnosis specification is known and their corresponding equations are introduced in the

- We develop a search-based testing technique for Simulink models that uses the existing alternative test objectives [15], [16], [21] to generate small and diverse test suites that

The principle adopted for the strategysuggested is based on monitoring of the average root meansquare (RMS) value of the stator current, which will beuseful as input data with the

Agarawal , “ Fault Diagnosis of Three Phase Induction Motor using Fuzzy Logic Controller and Sequence Analyzer”, MIT International Journal of Electrical and