@article {14433,
	title = {Structural Sampling for Statistical Software Testing},
	journal = {Probabilistic, Logical and Relational Learning-A Further Synthesis},
	year = {2008},
	month = {2008///},
	abstract = {Structural Statistical Software Testing exploits the control flow graph of the program being tested to construct test cases. While test cases can easily be extracted from {em feasible paths} in the control flow graph, that is, paths which are actually exerted for some values of the program input, the feasible path region is a tiny fraction of the graph paths (less than $10^{-5}]$ for medium size programs). The S4T algorithm presented in this paper aims to address this limitation; as an Active Relational Learning Algorithm, it uses the few feasible paths initially available to sample new feasible paths. The difficulty comes from the non-Markovian nature of the feasible path concept, due to the long-range dependencies between the nodes in the control flow graph. Experimental validation on real-world and artificial problems demonstrates significant improvements compared to the state of the art.},
	author = {Baskiotis,N. and Sebag,M. and De Raedt,L. and Dietterich,T. and Getoor, Lise and Kersting,K. and Muggleton,S. H}
}
