Data-flow testing approaches have been used for procedural and object-oriented (OO) programs, and empirically shown to be effective in detecting faults. However, few such approaches have been proposed for aspect-oriented (AO) programs. In an AO program, data-flow interactions can occur between the base classes and aspects, which can affect the behavior of both. Faults resulting from such interactions are hard to detect unless the interactions are specifically targeted during testing.;In this research, we propose a data-flow testing approach for AO programs. In an AO program, an aspect and a base class interact either through parameters passed from advised methods in the base class to the advice, or by the direct reading and writing of the base class state variables in the advice. We identify a group of def-use associations (DUAs) that are based on the base class state variables and propose a set of data-flow test criteria that require executing these DUAs. We identify fault types that result from incorrect data-flow interactions in AO programs and extend an existing AO fault model to include these faults. We implemented our approach in a tool that identifies the targeted DUAs by the proposed criteria, runs a test suite, and computes the coverage results.;We conducted an empirical study that compares the cost and effectiveness of the proposed criteria with two control-flow criteria. The empirical study is performed using four subject programs. We seeded faults in the programs using three mutation tools, AjMutator, Proteum/AJ, and muJava. We used a test generation tool, called RANDOOP, to generate a pool of random test cases. To produce a test suite that satisfies a criterion, we randomly selected test cases from the test pool until required coverage for a criterion is reached.;We evaluated three dimensions of the cost of a test criterion. The first dimension is the size of a test suite that satisfies a test criterion, which we measured by the number of test cases in the test suite. The second cost dimension is the density of a test case which we measured by the number of test cases in the test suite divided by the number of test requirements. The third cost dimension is the time needed to randomly obtain a test suite that satisfies a criterion, which we measured by (1) the number of iterations required by the test suites generator for randomly selecting test cases from a pool of test cases until a test criterion is satisfied, and (2) the number of the iterations per test requirement. Effectiveness is measured by the mutation scores of the test suites that satisfy a criterion. We evaluated effectiveness for all faults and for each fault type.;Our results show that the test suites that cover all the DUAs of state variables are more effective in revealing faults than the control-flow criteria. However, they cost more in terms of test suite size and effort. The results also show that the test suites that cover state variable DUAs in advised classes are suitable for detecting most of the fault types in the revised AO fault model.;Finally, we evaluated the cost-effectiveness of the test suites that cover all state variables DUAs for three coverage levels: 100%, 90%, and 80%. The results show that the test suites that cover 90% of the state variables DUAs are the most cost-effective.
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