A Genetic Algorithm based Approach for Test Data Generation in Basis Path Testing

TL;DR

This paper presents a genetic algorithm-based method for generating test data in basis path testing, aiming to automate and optimize the process to reduce testing effort and time.

Contribution

It introduces a novel application of genetic algorithms for test data generation based on basis paths, enhancing efficiency and reducing redundancy in software testing.

Findings

Genetic algorithm effectively generates suitable test data.

The approach reduces test effort and time.

Optimizes test data to avoid redundancy.

Abstract

Software Testing is a process to identify the quality and reliability of software, which can be achieved through the help of proper test data. However, doing this manually is a difficult task due to the presence of number of predicate nodes in the module. So, this leads towards a problem of NP-complete. Therefore some intelligence-based search algorithms have to be used to generate test data. In this paper, we use a soft computing based approach, genetic algorithm to generate test data based on the set of basis paths. This paper combines the characteristics of genetic algorithm with test data, making use of the merits of respective global and local optimization capability to improve the generation capacity of test data. This automated process of generating test data optimally helps in reducing the test effort and time of a tester. Finally, the proposed approach is applied for ATM withdrawal task. Experimental results show that genetic algorithm was able to generate suitable test data based on a fitness value and avoid redundant data by optimization.