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.

The paper reports on a first experimental comparison of software errors generated by real faults and by 1st-order mutations. The experiments were conducted on a program developed by a student from the industrial specification of a critical software from the civil nuclear field. Emphasis was put on the analysis of errors produced upon activation of 12 real faults by focusing on the mechanisms of error creation, masking, and propagation up to failure occurrence, and on the comparison of these errors with those created by 24 mutations. The results involve a total of 3730 errors recorded from program execution traces: 1458 errors were produced by the real faults, and the 2272 others by the mutations. They are in favor of a suitable consistency between errors generated by mutations and by real faults: 85% of the 2272 errors due to the mutations were also produced by the real faults. Moreover, it was observed that although the studied mutations were simple faults, they can create erroneous behaviors as complex as those identified for the real faults. This lends support to the representativeness of errors due to mutations.

The paper addresses the issue of testing programs written in the synchronous data flow language LUSTRE. We define a mixed strategy which combines statistical testing and deterministic extremal values testing. Statistical testing involves exercising a program with random inputs, the input distribution and the number of test data being determined according to test criteria. Three criteria based on the finite state automaton produced by the LUSTRE compiler are studied, the feasibility of the method for designing test sets according to them being exemplified on a real-case study. Then, mutation analysis (here, specific to LUSTRE) is used to assess the efficiency of the test sets. The results allow us (i) to define the most cost-effective criterion for designing efficient statistical test sets of reasonable size, and (ii) to show the high fault revealing power of the corresponding mixed strategy, killing the whole set of 310 mutants involved in the experiments.

The fault revealing power of different test patterns derived from ten structural test criteria currently referred to in unit testing is investigated. Experiments performed on four programs that are pieces of a real-life software system from the nuclear field are reported. Three test input generation techniques are studied: (1) deterministic choice, (2) random selection based on an input probability distribution determined according to the adopted structural test criterion, and (3) random selection from a uniform distribution on the input domain. Mutation analysis is used to assess the test set efficiency with respect to error detection. The experimental results involve a total of 2914 mutants. They show that structural statistical testing, which exhibits the highest mutation scores, leaving alive only six from 2816 nonequivalent mutants within short testing times, is the most efficient. A regards unit testing of programs whose structure remains tractable, the experiments show the adequacy of a fault removal strategy combining statistical and deterministic test patterns