A calculus for the random generation of labelled combinatorial structures
Theoretical Computer Science
Dynamically Discovering Likely Program Invariants to Support Program Evolution
IEEE Transactions on Software Engineering - Special issue on 1999 international conference on software engineering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Finite-time Analysis of the Multiarmed Bandit Problem
Machine Learning
A Generic Method for Statistical Testing
ISSRE '04 Proceedings of the 15th International Symposium on Software Reliability Engineering
DART: directed automated random testing
Proceedings of the 2005 ACM SIGPLAN conference on Programming language design and implementation
Statistical debugging: simultaneous identification of multiple bugs
ICML '06 Proceedings of the 23rd international conference on Machine learning
Approximate Satisfiability and Equivalence
LICS '06 Proceedings of the 21st Annual IEEE Symposium on Logic in Computer Science
Introduction to Automata Theory, Languages, and Computation (3rd Edition)
Introduction to Automata Theory, Languages, and Computation (3rd Edition)
EXE: automatically generating inputs of death
Proceedings of the 13th ACM conference on Computer and communications security
Exploring Multiple Execution Paths for Malware Analysis
SP '07 Proceedings of the 2007 IEEE Symposium on Security and Privacy
Software testing by active learning for commercial games
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
On prediction using variable order Markov models
Journal of Artificial Intelligence Research
A machine learning approach for statistical software testing
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Languages as hyperplanes: grammatical inference with string kernels
ECML'06 Proceedings of the 17th European conference on Machine Learning
Actively learning to verify safety for FIFO automata
FSTTCS'04 Proceedings of the 24th international conference on Foundations of Software Technology and Theoretical Computer Science
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Structural Statistical Software Testing (SSST) exploits the control flow graph of the program being tested to construct test cases. Specifically, SSST exploits the feasible paths in the control flow graph, that is, paths which are actually exerted for some values of the program input; the limitation is that feasible paths are massively outnumbered by infeasible ones. Addressing this limitation, this paper presents an active learning algorithm aimed at sampling the feasible paths in the control flow graph. The difficulty comes from both the few feasible paths initially available and the nature of the feasible path concept, reflecting the long-range dependencies among the nodes of the control flow graph. The proposed approach is based on a frugal representation inspired from Parikh maps, and on the identification of the conjunctive subconcepts in the feasible path concept within a Disjunctive Version Space framework. Experimental validation on real-world and artificial problems demonstrates significant improvements compared to the state of the art.