Automatic Test Data Generation Tool Based on Genetic Simulated Annealing Algorithm

This paper presents an automatic test data generation tool that aims at generating test data dynamically by reducing the problem of test data generation to one minimizing function. The tool is composed of three parts: program instrumentation module; test path generation module and test case generation module. In order to enhance the computational efficiency, we makes some improvements on the encoding, crossover probability and annealing gene of the genetic simulated annealing algorithm (GASS), and then use the results to generate the test data on the program instrumentation. Experimental results show this approach has better effect.

[1]  Bogdan Korel,et al.  Automated Software Test Data Generation , 1990, IEEE Trans. Software Eng..

[2]  Bryan F. Jones,et al.  Automatic structural testing using genetic algorithms , 1996, Softw. Eng. J..

[3]  John A. Clark,et al.  Automated program flaw finding using simulated annealing , 1998, ISSTA '98.

[4]  Gary McGraw,et al.  Generating Software Test Data by Evolution , 2001, IEEE Trans. Software Eng..

[5]  Carlos Urias Munoz,et al.  Automatic Generation of Random Self-Checking Test Cases , 1983, IBM Syst. J..

[6]  Lori A. Clarke,et al.  A System to Generate Test Data and Symbolically Execute Programs , 1976, IEEE Transactions on Software Engineering.

[7]  Roy P. Pargas,et al.  Test‐data generation using genetic algorithms , 1999, Softw. Test. Verification Reliab..

[8]  Phil McMinn,et al.  Search‐based software test data generation: a survey , 2004, Softw. Test. Verification Reliab..

[9]  Hiroshi Inamura,et al.  Trial-and-error method for automated test data generation and its evaluation , 1989, Systems and Computers in Japan.