We study the relationship between three properties of test suites: size, structural coverage, and fault-finding effectiveness. In particular, we study the question of whether achieving high coverage leads directly to greater effectiveness, or only indirectly through forcing a test suite to be larger. Our experiments indicate that coverage is sometimes correlated with effectiveness when size is controlled for, and that using both size and coverage yields a more accurate prediction of effectiveness than size alone. This in turn suggests that both size and coverage are important to test suite effectiveness. Our experiments also indicate that no linear relationship exists among the three variables of size, coverage and effectiveness, but that a nonlinear relationship does exist.

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The oracle problem is a crucial part in current research on software testing. In many situations available solutions like partial oracles can be used, however the suitability of a specific oracle depends on the application to be tested. Moreover, the inputs have a considerable impact on the effectiveness of the testing strategy. Therefore selecting an appropriate oracle as well as adequate inputs is essential in order to yield satisfying results.

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Mutation testing has been identified as one of the most effective techniques, in detecting faults. However, because of the large number of test elements that it introduces, it is regarded as rather expensive for practical use. Therefore, there is a need for testing strategies that will alleviate this drawback by selecting effective test data that will make the technique more practical. Such a strategy based on path selection is reported in this paper. A significant influence on the efficiency associated with path selection strategies is the number of test paths that must be generated in order to achieve a specified level of coverage, and it is determined by the number of paths that are found to be feasible. Specifically, a path selection strategy is proposed that aims at reducing the effects of infeasible paths and conversely developing effective and efficient mutation based tests. The results obtained from applying the method to a set of program units are reported and analysed presenting the flexibility, feasibility and practicality of the proposed approach.

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Mutation testing has historically been used to assess the fault-finding effectiveness of a test suite or other verification technique. Mutation analysis, rather, entails augmenting a test suite to detect all killable mutants. Concerns about the time efficiency of mutation analysis may prohibit its widespread, practical use. The goal of our research is to assess the effectiveness of the mutation analysis process when used by software testers to augment a test suite to obtain higher statement coverage scores. We conducted two empirical studies and have shown that mutation analysis can be used by software testers to effectively produce new test cases and to improve statement coverage scores in a feasible amount of time. Additionally, we find that our user study participants view mutation analysis as an effective but relatively expensive technique for writing new test cases. Finally, we have shown that the choice of mutation tool and operator set can play an important role in determining how efficient mutation analysis is for producing new test cases.

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