Historically, researchers have proposed and applied many techniques to reduce the cost of
mutation testing. It has become difficult to find all techniques and to understand the cost …

Mutation testing realizes the idea of using artificial defects to support testing activities.
Mutation is typically used as a way to evaluate the adequacy of test suites, to guide the …

Mutation analysis evaluates a testing or debugging technique by measuring how well it
detects mutants, which are systematically seeded, artificial faults. Mutation analysis is …

Deep Learning (DL) solutions are increasingly adopted, but how to test them remains a
major open research problem. Existing and new testing techniques have been proposed for …

Mutation testing offers concrete test goals (mutants) and a rigorous test efficacy criterion, but
it is expensive due to vast numbers of mutants, many of which are neither useful nor …

The research community has long recognized a complex interrelationship between fault
detection, test adequacy criteria, and test set size. However, there is substantial confusion …

Mutation testing realises the idea of fault-based testing, ie, using artificial defects to guide
the testing process. It is used to evaluate the adequacy of test suites and to guide test case …

Mutant selection refers to the problem of choosing, among a large number of mutants, the
(few) ones that should be used by the testers. In view of this, we investigate the problem of …

Mutation testing research has indicated that a major part of its application cost is due to the
large number of low utility mutants that it introduces. Although previous research has …

Mutation analysis is a well-studied, fault-based testing technique. It requires testers to
design tests based on a set of artificial defects. The defects help in performing testing …