Industrial experiences with automated regression testing of a legacy database application

摘要

This paper presents a practical approach and tool (DART) for functional black-box regression testing of complex legacy database applications. Such applications are important to many organizations, but are often difficult to change and consequently prone to regression faults during maintenance. They also tend to be built without particular considerations for testability and can be hard to control and observe. We have therefore devised a practical solution for functional regression testing that captures the changes in database state (due to data manipulations) during the execution of a system under test. The differences in changed database states between consecutive executions of the system under test, on different system versions, can help identify potential regression faults. In order to make the regression test approach scalable for large, complex database applications, classification tree models are used to prioritize test cases. The test case prioritization can be applied to reduce test execution costs and analysis effort. We report on how DART was applied and evaluated on business critical batch jobs in a legacy database application in an industrial setting, namely the Norwegian Tax Accounting System (SOFIE) at the Norwegian Tax Department (NTD). DART has shown promising fault detection capabilities and cost-effectiveness and has contributed to identify many critical regression faults for the past eight releases of SOFIE.