Data-Intensive Systems (DIS) are characterized by the need to collect, manipulate, store and retrieve of large amounts of data, and to analyze and apply complex business rules to such data.

Relevant examples are health, taxation, customs and excise application, where the underlying data-material typically scales with the size of the population.

Evidently, the tolerance for faults and errors in such applications is small.

In this project we study automated non-regression testing methods and evaluate the resulting cost-effectiveness and practicality of the methods in industrial settings. The overall aim is to automate functional testing at both module and system levels, as well as providing support for automated non-regression testing.

Objectives

• To increase the effectiveness of fault finding techniques for data-intensive systems
• To develop robust black-box and white-box non-regression testing techniques for administrative data-intensive systems
• To develop test case prioritization testing techniques for data-intensive systems