Factors Oriented Test Case Prioritization Technique in Regression Testing using Genetic Algorithm

Test case prioritization involves scheduling test cases in an order that increases the effectiveness in achieving some performance goals. One of the most important performance goals is the rate of fault detection. Test cases should run in an order that increases the possibility of fault detection and also that detects the most severe faults at the earliest in its testing life cycle. In this paper, we develop and validate requirement based system level test case prioritization scheme to reveal more severe faults at an earlier stage and to improve customer-perceived software quality using Genetic Algorithm (GA). For this, we propose a set of prioritization factors to design the proposed system. In our proposed technique, we refer to these factors as Prioritization Factors (PF). These factors may be concrete, such as test case length, code coverage, data flow, and fault proneness, or abstract, such as perceived code complexity and severity of faults, which prioritizes the system test cases based on the six factors: customer priority, changes in requirement, implementation complexity, completeness, traceability and fault impact. The goodness of these orderings was measured using an evaluation metric called APFD and PTR that will also be calculated.