Search-based software test data generation using evolutionary computation

TL;DR

This paper explores the use of evolutionary algorithms for automated test data generation, demonstrating their superior performance over random testing as program complexity increases.

Contribution

It introduces an evolutionary testing approach utilizing metaheuristic search methods for efficient test data generation in software testing.

Findings

GA-based testing outperforms random testing on complex programs

Evolutionary algorithms effectively navigate large input domains

Test data quality improves with evolutionary methods as complexity grows

Abstract

Search-based Software Engineering has been utilized for a number of software engineering activities. One area where Search-Based Software Engineering has seen much application is test data generation. Evolutionary testing designates the use of metaheuristic search methods for test case generation. The search space is the input domain of the test object, with each individual or potential solution, being an encoded set of inputs to that test object. The fitness function is tailored to find test data for the type of test that is being undertaken. Evolutionary Testing (ET) uses optimizing search techniques such as evolutionary algorithms to generate test data. The effectiveness of GA-based testing system is compared with a Random testing system. For simple programs both testing systems work fine, but as the complexity of the program or the complexity of input domain grows, GA-based testing system significantly outperforms Random testing.