An Efficient Test Data Generation Approach for Unit Testing

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 reduce the 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 are; Random testing, Symbolic execution and evolutionary testing. In this work, an enhanced and efficient Random test data generation approach is proposed and investigated on a suite of programs and its efficiency is compared with the Genetic algorithm which is an evolutionary approach. The inconsistency of random approach is that it is not capable of generating a specific set or combination of test cases for the program input variables and in search of these effective test cases multiple populations needs to be created that will increase the burden of size . So, in order to remove these inconsistencies from the test suite, it is seeded with a more effective set of test cases through our proposed approach in order to make test suite more granular and limit its size by not generating more populations in search of these effective test cases. In addition to the proposed approach, the classification of test adequacy criteria and issues with random, symbolic execution and genetic algorithm based test data