Test data generation is a key issue in the field of software testing. Adaptive random testing (ART) method has been proposed by Chen et al. to improve the fault-revealing ability of random testing. In the paper, we are mainly concerned with the partitioning-based adaptive random testing and present a new ART based on two-point partitioning. In the new algorithm, the current max-area region is p...

To reduce the number of test cases by utilized the minimum possible amount of time throughout the testing process of software and hardware is highly desirable. For ensuring the stability of the technique the combination of a complete set of available inputs is recommended to be executed. But generally an exhaustive numbers of test cases are hard to execute. Therefore, test data generation is an...

In this paper we describe an approach for automated modelbased test case and test data generation based on constraint types well known from bounded model checking. Our main contribution consists of a demonstration showing how this process can be considerably accelerated by using abstract interpretation techniques for preliminary explorations of the model state space. The techniques described su...

In general, test data generation techniques require an entire program path for automated test data generation. This paper presents a new way for generating test data automatically even without specifying a program path completely. The proposed method reduces the burden of selecting a program path and also makes it easy to generate test data according to various test adequacy criteria. For the e...

Test Data Generation is an intricate process which requires intensive manual labor and thus a lot of project time. There is an immediate need of finding out an effective technique for automating the process as manual Test Data Generation escalates the project cost. The paper proposes the use of Artificial Life in generating and minimizing the Test Cases. The work has been applied on some progra...

This paper presents a new technique for automatically generating test data. The technique is based on mutation analysis and creates test data that approximates relative adequacy. The technique is a fault-based technique that uses algebraic constraints to describe test cases designed to find particular types of faults. A set of tools, collectively called Godzilla, has been implemented that autom...

Glass-box test data generation (TDG) is the process of automatically generating test input data for a program by considering its internal structure. This is generally accomplished by performing symbolic execution of the program where the contents of variables are expressions rather than concrete values. The main idea in CLP-based TDG is to translate imperative programs into equivalent CLP ones ...