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Fault insertion based techniques have been used for measuring test adequacy and testability of programs. Mutation analysis inserts faults into a program with the goal of creating mutation-adequate test sets that distinguish the mutant from the original program. Software testability is measured by calculating the probability that a program will fail on the next test input coming from a predefined input distribution, given that the software includes a fault. Inserted faults must represent plausible errors. It is relatively easy to apply standard transformations to mutate scalar values such as integers, floats, and character data, because their semantics are well understood. Mutating objects that are instances of user defined types is more difficult. There is no obvious way to modify such objects in a manner consistent with realistic faults, without writing custom mutation methods for each object class. We propose a new object mutation approach along with a set of mutation operators and support tools for inserting faults into objects that instantiate items from common Java libraries heavily used in commercial software as well as user defined classes. Preliminary evaluation of our technique shows that it should be effective for evaluating real-world software testing suites.

In this paper, we present several complementary computational intelligence techniques that we explored in the field of .Net component testing. Mutation testing serves as the common backbone for applying classical and new artificial intelligence (AI) algorithms. With mutation tools, we know how to estimate the revealing power of test cases. With AI, we aim at automatically improving test case efficiency. We therefore looked first at genetic algorithms (GA) to solve the problem of test. The aim of the selection process is to generate test cases able to kill as many mutants as possible. We then propose a new AI algorithm that fits better to the test optimization problem, called bacteriological algorithm (BA): BAs behave better that GAs for this problem. However, between GAs and BAs, a family of intermediate algorithms exists: we explore the whole spectrum of these intermediate algorithms to determine whether an algorithm exists that would be more efficient than BAs.: the approaches are compared on a .Net system.

Program mutation is a fault-based technique for measuring the effectiveness of test cases that, although powerful, is computationally expensive. The principal expense of mutation is that many faulty versions of the program under test, called mutants, must be created and repeatedly executed. This paper describes a tool, called JavaMut, that implements 26 traditional and object-oriented mutation operators for supporting mutation analysis of Java programs. The current version of that tool is based on syntactic analysis and reflection for implementing mutation operators. JavaMut is interactive; it provides a graphical user interface to make mutation analysis faster and less painful. Thanks to such automated tools, mutation analysis should be achieved within reasonable costs.

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The level of confidence in a software component is often linked to the quality of its test cases. This quality can in turn be evaluated with mutation analysis: faulty components (mutants) are systematically generated to check the proportion of mutants detected ("killed") by the test cases. But while the generation of basic test cases set is easy, improving its quality may require prohibitive effort. This paper focuses on the issue of automating the test optimization. We looked at genetic algorithms to solve this problem and modeled it as follows: a test case can be considered as a predator while a mutant program is analogous to a prey. The aim of the selection process is to generate test cases able to kill as many mutants as possible. To overcome disappointing experimentation results on the studied .Net system, we propose a slight variation on this idea, no longer at the "animal" level (lions killing zebras) but at the bacteriological level. The bacteriological level indeed better reflects the test case optimization issue: it introduces of a memorization function and the suppresses the crossover operator. We describe this model and show how it behaves on the case study.

Although program faults are widely studied, there are many aspects of faults that we still do not understand, particularly about OO software. In addition to the simple fact that one important goal during testing is to cause failures and thereby detect faults, a full understanding of the characteristics of faults is crucial to several research areas. The power that inheritance and polymorphism brings to the expressiveness of programming languages also brings a number of new anomalies and fault types. In prior work we presented a fault model for the appearance and realization of OO faults that are specific to the use of inheritance and polymorphism. Many of these faults cannot appear unless certain syntactic patterns are used. The patterns are based on language constructs, such as overriding methods that directly define inherited state variables and non-inherited methods that call inherited methods. If one of these syntactic patterns is used, then we say the software contains an anomaly and possibly a fault. We describe the syntactic patterns for each OO fault type. These syntactic patterns can potentially be found with an automatic tool. Thus, faults can be uncovered and removed early in development.