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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The effectiveness of a test data adequacy criterion for a given program and specification is the probability that a test set satisfying the criterion will expose a fault. Experiments were performed to compare the effectiveness of the mutation testing and all-uses test data adequacy criteria at various coverage levels, for randomly generated test sets. Large numbers of test sets were generated and executed, and for each, the proportion of mutants killed or def-use associations covered was measured. This data was used to estimate and compare the effectiveness of the criteria. The results were mixed: at the highest coverage levels considered, mutation was more effective than all-uses for five of the nine subjects, all-uses was more effective than mutation for two subjects, and there was no clear winner for two subjects. However, mutation testing was much more expensive than all-uses. The relationship between coverage and effectiveness for fixed-sized test sets was also explored and was found to be nonlinear and, in many cases, nonmonotonic.

This paper presents a new approach which allows VLSI digital signal processors (DSP) to be totally tested concurrently within useful computation. This approach uses a software technique called Mutation resting which has been successfully applied to hardware devices. Based on realistic examples of signal processing applications and state-of-the-art DSPs, the approach is shown highly efficient in terms of fault coverage and fault latency.

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Fault injection is important to evaluating the dependability of computer systems. Researchers and engineers have created many novel methods to inject faults, which can be implemented in both hardware and software. The contrast between the hardware and software methods lies mainly in the fault injection points they can access, the cost and the level of perturbation. Hardware methods can inject faults into chip pins and internal components, such as combinational circuits and registers that are not software-addressable. On the other hand, software methods are convenient for directly producing changes at the software-state level. Thus, we use hardware methods to evaluate low-level error detection and masking mechanisms, and software methods to test higher level mechanisms. Software methods are less expensive, but they also incur a higher perturbation overhead because they execute software on the target system.

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Two experimental comparisons of data flow and mutation testing are presented. These techniques are widely considered to be effective for unit-level software testing, but can only be analytically compared to a limited extent. We compare the techniques by evaluating the effectiveness of test data developed for each. We develop ten independent sets of test data for a number of programs: five to satisfy the mutation criterion and five to satisfy the all-uses data-flow criterion. These test sets are developed using automated tools, in a manner consistent with the way a test engineer might be expected to generate test data in practice. We use these test sets in two separate experiments. First we measure the effectiveness of the test data that was developed for one technique in terms of the other. Second, we investigate the ability of the test sets to find faults. We place a number of faults into each of our subject programs, and measure the number of faults that are detected by the test sets. Our results indicate that while both techniques are effective, mutation-adequate test sets are closer to satisfying the data flow criterion, and detect more faults.