Uniform random sampling of traces in very large models
Proceedings of the 1st international workshop on Random testing
Test automation for hybrid systems
Proceedings of the 3rd international workshop on Software quality assurance
Directed random reduction of combinatorial test suites
Proceedings of the 2nd international workshop on Random testing: co-located with the 22nd IEEE/ACM International Conference on Automated Software Engineering (ASE 2007)
Uniform random walks in very large models
Proceedings of the 2nd international workshop on Random testing: co-located with the 22nd IEEE/ACM International Conference on Automated Software Engineering (ASE 2007)
Coverage-biased Random Exploration of Models
Electronic Notes in Theoretical Computer Science (ENTCS)
Model Driven Testing Based on Test History
Transactions on Petri Nets and Other Models of Concurrency I
A machine learning approach for statistical software testing
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Structural statistical software testing with active learning in a graph
ILP'07 Proceedings of the 17th international conference on Inductive logic programming
Uniform Monte-Carlo model checking
FASE'11/ETAPS'11 Proceedings of the 14th international conference on Fundamental approaches to software engineering: part of the joint European conferences on theory and practice of software
ICTSS'11 Proceedings of the 23rd IFIP WG 6.1 international conference on Testing software and systems
Using CHRs to generate functional test cases for the java card virtual machine
PADL'06 Proceedings of the 8th international conference on Practical Aspects of Declarative Languages
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This paper addresses the problem of selecting finite test sets and automating this selection. Among these methods, some are deterministic and some are statistical. The kind of statistical testing we consider has been inspired by the work of Thevenod-Fosse and Waeselynck. There, the choice of the distribution on the input domain is guided by the structure of the program or the form of its specification. In the present paper, we describe a new generic method for performing statistical testing according to any given graphical description of the behavior of the system under test. This method can be fully automated. Its main originality is that it exploits recent results and tools in combinatorics, precisely in the area of random generation of combinatorial structures. Uniform random generation routines are used for drawing paths from the set of execution paths or traces of the system under test. Then a constraint resolution step is performed, aiming to design a set of test data that activate the generated paths. This approach applies to a number of classical coverage criteria. Moreover, we show how linear programming techniques may help to improve the quality of test, i.e. the probabilities for the elements to be covered by the test process. The paper presents the method in its generality. Then, in the last section, experimental results on applying it to structural statistical software testing are reported.