期刊名称：Software Engineering : an International Journal
电子版ISSN：2249-9342
出版年度：2012
卷号：2
期号：1
页码：20-39
出版社：Delhi Technological Universiity
摘要：The length and the complexity of the software are rising day by day. This rising complexity has increased the demand for techniques that can generate test data effectively. Test data generation techniques selects from the input domain of the program, those input values that satisfies a pre-defined testing criteria. In this paper, we propose a new test data generation algorithm. Our algorithm generates test data using adequacy based testing criteria that aims to generate an adequate test data set by using the concept of mutation analysis. In general, mutation analysis is applied after the test data is generated. But, our algorithm applies mutation analysis at the time of generating test data only rather than applying it after the generation of test data. Incorporation of mutation analysis at the time of test data generation leads to the generation of test data that is itself adequate and hence we need not check for its adequacy after its generation. We also make use of genetic algorithms that explore the complete domain of the program to generate near-global optimum test data set. In order to analyze our algorithm, we evaluate it using fifty real time programs written in C language. The program set contains programs ranging from 35 to 350 lines of source code and includes from very basic to very complex programs. We compare our algorithm with path testing and condition testing techniques (that uses reliability based testing criteria) for these fifty programs in two categories viz. number of generated test cases and the time taken to generate test cases. The results suggest that our adequacy based algorithm is better than the reliability based path testing and condition testing techniques in both of these categories. Thus this algorithm may significantly reduce the time of test data generation.ge:
