Towards test suite reduction using maximal frequent data mining concept
Preethi Harris, Raju Nedunchezhian · International Journal of Computer Applications in Technology · 2015
All the industries are going through a revolution where software has become an essential component. Software testing is important to produce more reliable systems. Over the years this testing activity has evolved into a major resource consumer and cost driver for the software industries. This invariably leads to the addressing of key issues for efficient test case selection and managing test suite size. In this paper, a data mining-based algorithm has been proposed to address the issue of test suite optimisation. This algorithm focuses on selecting maximal frequent test sets for dataflow testing with Define Use (DU) pairs as requirements. Experimentation has been carried out to compare the relative performance and effectiveness of the proposed test suite reduction algorithm with the state-of-the-art algorithms: Harrold Gupta and Soffa (HGS) and Bi-Objective Greedy (BOG) using test metrics.