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Experimental And Quasi Experimental Designs For Research

Fundamental statistics in psychology and education.

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 generation of test cases, and to support experimentation. Mutation has reached a maturity phase and gradually gains popularity both in academia and in industry. This chapter presents a survey of recent advances, over the past decade, related to the fundamental problems of mutation testing and sets out the challenges and open problems for the future development of the method. It also collects advices on best practices related to the use of mutation in empirical studies of software testing. Thus, giving the reader a “mini-handbook”-style roadmap for the application of mutation testing as experimental methodology.

/pdf/mutation-testing-advances-an-analysis-and-survey-2wtvd7tjwe.pdf

Mutation Testing Advances: An Analysis and Survey

We apply a model checker to the problem of test generation using a new application of mutation analysis. We define syntactic operators, each of which produces a slight variation on a given model. The operators define a form of mutation analysis at the level of the model checker specification. A model checker generates countersamples which distinguish the variations from the original specification. The countersamples can easily be turned into complete test cases, that is, with inputs and expected results. We define two classes of operators: those that produce test cases from which a correct implementation must differ, and those that produce test cases with which it must agree. There are substantial advantages to combining a model checker with mutation analysis. First, test case generation is automatic; each countersample is a complete test case. Second, in sharp contrast to program-based mutation analysis, equivalent mutant identification is also automatic. We apply our method to an example specification and evaluate the resulting test sets with coverage metrics on a Java implementation.

Using Model Checking to Generate Tests from Specifications | NIST

We transpose the theory of rank metric. Gabidulin codes (8) W. C. Huffman and V. Pless, Fundamentals of Error-Correcting Codes. Cambridge. Leech's construction is based on the binary Golay code of length. 24. Later on (13) V.Pless, An Introduction to the Theory of Error-Correcting Codes, Wi. Download full text in PDF Opens in a new window. R. Brualdi and V. Pless, On the length of codes with a given covering radius, Preprint. Introduction to the Theory of Functions of One VariableA. Characterization of (2(q + 1) + 2, 2, t, q)-min-hyper in PG(t, q) (t 3, q 5) and its applications to error-correcting codes. of Coding Theory. 5. 1.

http://ieeexplore.ieee.org/iel5/5/31339/01457228.pdf

Introduction to the theory of error-correcting codes

Mutation testing is a fault-based software testing technique in which a large number of mutants are generated in order to assess the adequacy of test cases devised. One of the daunting problems in this area consists in determining whether a mutant can be killed by a test case or it cannot be killed easily because the mutant is semantically equivalent to the original programme. A possible solution, as proposed in this paper, is to measure the complexity of each mutant and prioritize them according to how easy or hard they are to be exposed. As a result, using a proper metric based on the mutants' complexity, the tester may decide whether to focus on killing easy or hard mutants first.

MuRanker: a mutant ranking tool

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MuRanker: a mutant ranking tool