A Code Profiling Model for StART

Abstract

Abstract Test case selection in software testing is one of the process that support to quality in finding bug. Decreasing number of bug may increase quality of software. Many strategies have been proposed by researcher for software testing. Specially to avoid exhausted testing which all the testers take into account which may increase time and performance. One of the strategy is StART. The code profiling is a part of StART, which is a selection test cases strategy using code profiling. The strategy is based on the adaptive random testing and support with code profiling in the strategy. The aim of this paper is to show the code profiling model. The selection of domain area to test is based on the highest probability in the code profiling result. The probability of in the code profiling is calculated and the highest value to be chosen as the first area to test. The value to be chosen as a domain area for the first to be tested in SUT. This model used AspectJ as case study to show the model can be implemented for a new programming paradigm. Tetris selected as case study to show the model flow. From the case study of Tetris, the model shows the area to be tested first is in the package Gui, for aspect menu and advice after the probability value shows the highest result. For future study, this model need to test their efficiency of their strategy.