Early fault detection model using integrated and cost-effective test case prioritization

Abstract

Regression testing is one of the important software maintenance activities that let the software tester to ensure the quality and reliability of modified program. Although the regression testing is expensive, it is necessary to validate the software after every modification. To reduce the cost of regression testing, software tester may utilize test cases prioritization techniques. One potential goal prioritization is to increase a test suite’s rate of fault detection. An improved rate of fault detection can provide earlier feedback on the system, enabling earlier debugging. APFD (Average Percentage of Fault Detected) metric is used to measure the test suite’s fault detection rate. This paper presents an integrated test case prioritization approach to increase the test suite’s fault detection rate. Three important factors such as program change level (PCL), test suite change level (TCL) and test suite size (TS) are considered to prioritize test cases. Proposed approach is applied on different in-house programs to validate its accuracy. Model results are found to be promising when compared with optimal prioritization techniques which always results an upper bound of APFD values.