Framework for Intelligent Meaningful Test Data Generation model-IMTDG

The generation of actual test data is one of the difficult and expensive parts of applying software testing techniques. Many of the current test data generators focus firstly on the data type and structure, regardless of the meaning, and secondly on developer or database administrator viewpoints, regardless of user concerns. This leads to generating a high amount of meaningless test data, especially when generating non-numeric data, which may not reflect the specification or environment of the population under test, besides the reduction of user confidence in the generated data and in testing at all! In this paper, we propose a framework for an intelligent meaningful test data generation (IMTDG) model with the aim of increasing the users' confidence in software testing. The model uses samples of real data as resource data and a set of efficient generation techniques based on statistical methods, such as permutations, combinations, sampling and statistical distributions. Selection of the most suitable structure and generation technique is based on one of the intelligent soft computing techniques, such as fuzzy logic, neural networks or genetic algorithms. The generated test data is close to the real-world data for testing processes with the ability to simulate real environments