Artificial life and cellular automata based automated test case generator

Abstract

Manual test data generation is carried out by using the ability of neurons to recognize patterns. The nervous system and the brain coordinate to generate test cases, which are capable of finding potential faults. Automated test data generators lack the ability to produce efficient test cases because they do not imitate natural processes. This paper proposes using Artificial Life based systems for generating test cases. Cellular Automata and Langton's loop have been used to accomplish the above task. Cellular Automata are parallel distributed systems capable of reproducing using self generated patterns. These fascinating techniques have been amalgamated with standard test data generation techniques to give rise to a methodology, which generates test cases for white box testing. Langton's Loops have been used to generate test cases for Black Box Testing. The approach has been verified on a set of 20 programs. The programs have been selected on the basis of their Lines of Code and utility. The results obtained have been verified using Average Probability of Fault Detection. This paper also proposes a new framework capable of crafting test cases taking into account the oracle cost.