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• W2296141424 abstract "Ensuring the functional quality of database applications is a very important problem in software testing, yet few innovative solutions and empirical studies are reported on the subject. Database applications are widely adopted, for example in public administrations and banks, as they need to process large amounts of transactions efficiently for a large population and store large amounts of data. Such applications are often highly automated in order to efficiently cope with a large number of transactions, and are usually difficult to maintain and change. In order to preserve system quality through frequent system releases, a thorough, systematic, and automated regression test approach is needed for such applications, as they tend to provide core business value to their organizations. The objective of this thesis is to help scale functional system-level regression testing of database applications through cost-effective automation. We propose a black-box approach that relies on classification trees to model the input domain of the system under test. We use the classification tree models as basis for automatically selecting abstract regression test cases, and either generate test data automatically according to the model specifications or rely on production data (data cloned from the production environment) that match the model specifications. Regression testing is carried out by running the selected test cases on consecutive versions of the system under test, while automatically capturing changes in the database state during system execution. The captured database manipulations for each test case are automatically compared across system versions, and test cases that deviate between two system versions are either due to anticipated changes in the release, or regression faults. The resulting deviations from a regression test are clustered according to their output characteristics, so that deviations resulting from the same change or fault (ideally) are contained in the same cluster. These clusters are then used to minimize the effort required to analyze deviations. In order to evaluate our approach, we conducted a large-scale case study in a real development environment at the Norwegian Tax Department. The Norwegian Tax Department maintains several database applications, which are built on standard and widely used database technology and are representative of many such applications in public administrations. Together with the Norwegian Tax Department, we developed an industry-" @default.
• W2296141424 created "2016-06-24" @default.
• W2296141424 creator A5001146387 @default.
• W2296141424 date "2016-01-01" @default.
• W2296141424 modified "2023-09-27" @default.
• W2296141424 title "Automated regression testing of database applications" @default.
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• W2296141424 hasPublicationYear "2016" @default.
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