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Title: Mutation analysis of dynamically typed programs
Author: Abu Hashish, Nabil
Awarding Body: University of Hull
Current Institution: University of Hull
Date of Award: 2013
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The increasing use of dynamically typed programming languages brings a new challenge to software testing. In these languages, types are not checked at compile-time. Type errors must be found by testing and in general, programs written in these languages require additional testing compared to statically typed languages. Mutation analysis (or mutation testing) has been shown to be effective in testing statically (or strongly) typed programs. In statically typed programs, the type information is essential to ensure only type-correct mutants are generated. Mutation analysis has not so far been fully used for dynamically typed programs. In dynamically typed programs, at compile-time, the types of the values held in variables are not known. Therefore, it is not clear if a variable should be mutated with number, Boolean, string, or object mutation operators. This thesis investigates and introduces new approaches for the mutation analysis of dynamically typed programs. The first approach is a static approach that employs the static type context of variables to determine, if possible, type information and generate mutants in the manner of traditional mutation analysis. With static mutation there is the danger that the type context does not allow the precise type to be determined and so type-mutations are produced. In a type-mutation, the original and mutant expressions have a different type. These mutants may be too easily killed and if they are then they represent incompetent mutants that do not force the tester to improve the test set. The second approach is designed to avoid type-mutations. This approach requires that the types of variables are discovered. The types of variables are discovered at run-time. Using type information, it is possible to generate only type-correct mutants. This dynamic approach, although more expensive computationally, is more likely to produce high quality, difficult to kill, mutants.
Supervisor: Bottaci, Leonardo Sponsor: University of Hull ; Al-Isra University (Jordan)
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Computer science