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This paper presents a novel approach to unit testing that lets users of deployed software assist in performing mutation testing of the software. Our technique, MUGAMMA, provisions a software system so that when it executes in the field, it will determine whether users' executions would have killed mutants (without actually executing the mutants), and if so, captures the state information about those executions. In the absence of bug reports, knowledge of executions that would have killed mutants provides additional confidence in the system over that gained by the testing performed before deployment. Captured information about the state before and after execution of units (e.g., methods) can be used to construct test cases for use in unit testing when changes are made to the software. The paper also describes our prototype MUGAMMA implementation along with a case study that demonstrates its potential efficacy.

The effectiveness of mutation testing depends heavily on the types of faults that the mutation operators are designed to represent. Thus, the quality of the mutation operators is key to mutation testing. Although, mutation operators for object-oriented languages have previously been presented, little research has been done to show the usefulness of the class mutation operators. To assess the usefulness of class mutation operators, we conducted two empirical studies. In the first study, we examine the number and kinds of mutants that are generated for object-oriented programs. In the second study, we investigate the way in which class mutation operators model faults that are not detected by traditional mutation testing. We conducted our studies using a well-known object-oriented system, BCEL.

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We present a fault-classification scheme and a fault-seeding method that are based on the manifestation of faults in the program dependence graph (PDG). We enhance the domain/computation fault classification scheme developed by Howden to further characterize faults as structural and statement-level depending on the differences between the PDG for the original program and the PDG for the faulty program. We perform transformations on the PDG to produce the different types of faults described in our PDG-based fault-classification scheme. To demonstrate the usefulness of our technique, we implemented a fault seeder to embed faults in C programs. Our fault seeder makes controlled fault transformations to the PDG for a C program, and generates C code from the transformed PDG. The current version of the fault seeder creates multiple fault-seeded versions of the original program, each with one known fault. To demonstrate the operation of the fault seeder, we used it to perform a study of the effectiveness of dataflow testing and mutation testing using a set of faulty programs generated by our fault seeder. We also used the faulty programs to determine the mutation adequacy and detaflow adequacy of the fault-detecting test sets.

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Mutation analysis is a powerful technique for assessing and improving the quality of test data used to unit test software. Unfortunately, current automated mutation analysis systems suffer from severe performance problems. This paper presents a new method for performing mutation analysis that uses program schemata to encode all mutants for a program into one metaprogram, which is subsequently compiled and run at speeds substantially higher than achieved by previous interpretive systems. Preliminary performance improvements of over 300% are reported. This method has the additional advantages of being easier to implement than interpretive systems, being simpler to port across a wide range of hardware and software platforms, and using the same compiler and run-time support system that is used during development and/or deployment.