Multiple fault localization using constraint programming and pattern mining

Spectrum-based fault localiza- tion (SBFL) (e.g. [1], [18]) is a class of popular fault localization approaches that take as input a set of failing and passing test case executions,

Recall that our core intuition is that the spectrum-based fault localization algorithm misses causal information about the propagation of a fault in the program. Using our

The rules (2) are no more efficient for multiple fault isolation because the occurrence of two simultaneous faults in the system leads to a new fault

Running the bacteriologic algorithm 4 times, with an initial test suite randomly generated, we obtained 4 test suites composed of 14 to 18 test cases, each covering around 87% of

In addition, in their extended analysis of spatial correlations of slip maps for 44 earthquakes, Mai & Beroza (2002) found that the heterogeneous slip distribution follows

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 − |,

To the best of our knowledge, Xuan and Monperrus propose in 2014 the first and unique work combining multiple ranking metrics [19]. M ULTRIC is based on a passive learning

In the sequel, we will explain our running example for the Fault Localization Problem in Section 2, give a brief introduction to Formal Concept Analysis and Association Rules in