Software testing techniques (2nd ed.)
Software testing techniques (2nd ed.)
Proceedings of the third international conference on Genetic algorithms
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Randomized algorithms
Introduction to Algorithms
IEEE Transactions on Software Engineering
Search-based software test data generation: a survey: Research Articles
Software Testing, Verification & Reliability
On the Choice of the Offspring Population Size in Evolutionary Algorithms
Evolutionary Computation
Runtime Analysis of the (μ+1) EA on Simple Pseudo-Boolean Functions
Evolutionary Computation
Test input generation for java containers using state matching
Proceedings of the 2006 international symposium on Software testing and analysis
Proceedings of the 2007 international symposium on Software testing and analysis
Search based software testing of object-oriented containers
Information Sciences: an International Journal
ICSTW '08 Proceedings of the 2008 IEEE International Conference on Software Testing Verification and Validation Workshop
SSBSE '09 Proceedings of the 2009 1st International Symposium on Search Based Software Engineering
ASE '08 Proceedings of the 2008 23rd IEEE/ACM International Conference on Automated Software Engineering
Theoretical analysis of local search in software testing
SAGA'09 Proceedings of the 5th international conference on Stochastic algorithms: foundations and applications
Testing container classes: random or systematic?
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
EuroGP'10 Proceedings of the 13th European conference on Genetic Programming
Hi-index | 0.00 |
Software testing can be re-formulated as a search problem, hence search algorithms (e.g., Genetic Algorithms) can be used to tackle it. Most of the research so far has been of empirical nature, in which novel proposed techniques have been validated on software testing benchmarks. However, only little attention has been spent to understand why meta-heuristics can be effective in software testing. This insight knowledge could be used to design novel more successful techniques. Recent theoretical work has tried to fill this gap, but it is very complex to carry out. This has limited its scope so far to only small problems. In this paper, we want to get insight knowledge on a difficult software testing problem. We combine together an empirical and theoretical analysis, and we exploit the benefits of both.