Our research in the past has been focused on the use of database/knowledge base technologies to automate software testing tasks in order to provide a more cost-effective approach to managing software faults. Our recent interest is to build a knowledge-based T&E environment such that tests, faults, and diagnosis associated with a program at different levels of abstraction over the time are presented and stored as objects. These objects can be automatically tracked and analyzed so that patterns and knowledge can be extracted to facilitate test and evaluation of future software products and to facilitate the maintenance of existing products.

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To assure the quality of software by running test cases and evaluating the results is one of the difficult parts of the entire software development project. The difficulty usually comes from the lack of appropriate supporting tools and the complexity of the software. In the past ad hoc supporting tools were made for each project and test results were usually not used across projects. This conventional way of test and evaluation (T&E) is time consuming, and the most important decision "When is this software ready to ship?" is left to the engineers depending on their experiences. Our objective is to build a knowledge-based T&E environment such that tests cases, test results, object snapshots and other information are accumulated in a database. These longitudinal data can be automatically tracked and analyzed to provide decision support information. As a result, test results can be reviewed repeatedly and software quality can be assured by analyzing these data from various perspectives.