(1)Fault localization using itemset mining under constraints

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

1 For instance, if we post the global constraint for frequent closed itemsets (FCIs) proposed in [6] in a CP model and if in addition we post the constraint specifying that the user

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,

In order to introduce softness in this context, we propose two kinds of soft skypatterns: the edge-skypatterns that belongs to the edge of the dominance area (see Section 3.1) and the

Seismicity and fault plane solutions of earthquakes at the intersection between the Main Recent Fault (a right-lateral strike-slip fault that bounds the Zagros to the NE) and

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 − |,

Data generation allows us to both break the bottleneck of too few data sets (or data sets with a too narrow range of characteristics), and to understand how found patterns relate to

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