Name
Abstract | ||
|---|---|---|
Structural Statistical Software Testing exploits the control flow\r\ngraph of the program being tested to construct test cases. \r\nWhile test cases can easily be extracted from {em feasible paths} in the control \r\nflow graph, that is, paths which are actually exerted for some \r\nvalues of the program input, the feasible path region is a tiny fraction \r\nof the graph paths (less than $10^{-5}]$ for medium size programs).\r\nThe S4T algorithm presented in this paper aims to address this limitation; \r\nas an Active Relational Learning Algorithm, it uses the few feasible paths\r\ninitially available to sample new feasible paths. The difficulty comes \r\nfrom the non-Markovian nature of the feasible path concept, due to the \r\nlong-range dependencies between the nodes in the control flow graph. \r\nExperimental validation on real-world and artificial problems \r\ndemonstrates significant improvements compared to the state of the art. |
Year | Venue | Field |
|---|---|---|
2007 | Probabilistic, Logical and Relational Learning - A Further Synthesis | Any-angle path planning,Statistical software,Computer science,White-box testing,Sampling (statistics),Artificial intelligence,Basis path testing,Machine learning |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
1 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nicolas Baskiotis | 1 | 119 | 11.73 |
| Michèle Sebag | 2 | 1547 | 138.94 |