An Efficient Test Data Generation Approach for Unit Testing
Journal Title: IOSR Journals (IOSR Journal of Computer Engineering) - Year 2016, Vol 18, Issue 4
Abstract
Abstract: To ensure the delivery of high-quality software, software testing plays the vital role. One of the major time-consuming and expensive activities in software testing is the generation of test data. Test data generation activity has a strong impact on the effectiveness and efficiency of the whole testing process. In order to reducethe cost and time involved in the process of test data generation, researchers and practitioners have tried to automate it. In literature, many such techniques have been developed and the most commonly used ones are; Random testing, Symbolic execution and evolutionary testing. In this work, an enhanced and efficient Random test data generation approach is proposed and investigated for a suite of programs and its efficiency is compared with the Genetic algorithm which is an evolutionary approach. The inconsistency of randomapproach is that it is not capable of generating a specific set or combination of test cases for the program input variables. So, in order to remove this inconsistency from the test suite, it is seeded with a more effective set of test cases through our proposed approach. In addition to the proposed approach, the classification of test adequacy criteria and issues with random, symbolic execution and genetic algorithm based test data generation techniques are also provided and highlighted.
Authors and Affiliations
Anil Kumar Gupta, Fayaz Ahmad Khan
Keywords
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