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Title: Strong mutation testing strategies
Author: Duncan, Ishbel M. M.
ISNI:       0000 0001 3434 8695
Awarding Body: Durham University
Current Institution: Durham University
Date of Award: 1993
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Mutation Testing (or Mutation Analysis) is a source code testing technique which analyses code by altering code components. The output from the altered code is compared with output from the original code. If they are identical then Mutation Testing has been successful in discerning a weakness in either the test code or the test data. A mutation test therefore helps the tester to develop a program devoid of simple faults with a well developed test data set. The confidence in both program and data set is then increased. Mutation Analysis is resource intensive. It requires program copies, with one altered component, to be created and executed. Consequently, it has been used mainly by academics analysing small programs. This thesis describes an experiment to apply Mutation Analysis to larger, multi-function test programs. Mutations, alterations to the code, are induced using a sequence derived from the code control flow graph. The detection rate of live mutants, programs whose output match the original, was plotted and compared against data generated from the standard technique of mutating in statement order. This experiment was repeated for different code components such as relational operators, conditional statement or pointer references. A test was considered efficient if the majority of live mutants was detected early in the test sequence. The investigations demonstrated that control flow driven mutation could improve the efficiency of a test. However, the experiments also indicated that concentrations of live mutants of a few functions or statements could effect the efficiency of a test. This conclusion lead to the proposal that mutation testing should be directed towards functions or statements containing groupings of the code component that give rise to the live mutants. This effectively forms a test focused onto particular functions or statements.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Software testing