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 …

Learning-based program repair has achieved good results in a recent series of papers. Yet,
we observe that the related work fails to repair some bugs because of a lack of knowledge …

Mutation analysis assesses a test suite's adequacy by measuring its ability to detect small
artificial faults, systematically seeded into the tested program. Mutation analysis is …

Traditionally, mutation testing generates an abundance of small deviations of a program,
called mutants. At industrial systems the scale and size of Facebook's, doing this is …

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 …

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 …

In mutation testing, the quality of a test suite is evaluated by introducing faults into a program
and determining whether the program's tests detect them. Most existing approaches for …