On the Inference of Automatic Generation of Software Tests

Abstract

Automatic generation of software test cases is studied. Within software engineering the software testing phase aims to find errors in software. However, achieving a fully tested program is a hard problem. Moreover, automation of test generation seems to be useful in order to reduce the software development cost. A scheme for the test case generation using tabu search is presented by Srivastava et. al. in 2009. In this paper, we work on their scheme. The scheme is implemented by varying slightly and comprehended by taking example for the further improvement. The study confirms that for assigning priority among the generated test cases for complex problems dynamic clustering is more suitable as compared with k-means clustering.