2008/01/01 by Nicolas Baskiotis, Baskiotis, Nicolas, Michèle Sébag +1
Computer Science · #Active Relational Learning #Autonomic Computing #Parikh Maps #Software Engineering Research #Software Reliability and Analysis Research #Software Testing #Software Testing and Debugging Techniques
paper · doi:10.4230/dagsemproc.07161.9
openalex publication_date 2008/01/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.