Multiple-Searching Genetic Algorithm for Whole Test Suites

Wanida Khamprapai,Chi-En Tsai,Cheng-Fa Tsai,Paohsi Wang

doi:10.3390/electronics10162011

Wanida Khamprapai, Chi-En Tsai + Show 2 more

Open Access

https://doi.org/10.3390/electronics10162011

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Abstract

A test suite is a set of test cases that evaluate the quality of software. The aim of whole test suite generation is to create test cases with the highest coverage scores possible. This study investigated the efficiency of a multiple-searching genetic algorithm (MSGA) for whole test suite generation. In previous works, the MSGA has been effectively used in multicast routing of a network system and in the generation of test cases on individual coverage criteria for small- to medium-sized programs. The performance of the algorithms varies depending on the problem instances. In this experiment were generated whole test suites for complex programs. The MSGA was expanded in the EvoSuite test generation tool and compared with the available algorithms on EvoSuite in terms of the number of test cases, the number of statements, mutation score, and coverage score. All algorithms were evaluated on 14 problem instances with different corpus to satisfy multiple coverage criteria. The problem instances were Java open-source projects. Findings demonstrate that the MSGA generated test cases reached greater coverage scores and detected a larger number of faults in the test class when compared with the others.

Highlights

In software testing, genetic algorithms (GAs) have successfully achieved the maximum coverage criterion in object-oriented classes [1,2,3,4]
Test suite generation has been a major topic in software testing research in creating efficient test cases
By investigating the performance of the multiple-searching genetic algorithm (MSGA) to generate whole test suites for Java classes and complex programs, this experiment established that test cases of the MSGA revealed a larger number of faults and executed more instructions in the test class than other algorithms

Summary

Introduction

Genetic algorithms (GAs) have successfully achieved the maximum coverage criterion in object-oriented classes [1,2,3,4]. The coverage criterion is a definition for which parts of the program are to be executed and whether the set of test cases can be executed as defined [5,6]. There are a variety of coverage criteria for testing (e.g., branch, line, mutation). EvoSuite [7] is an automatic tool to generate whole test suites for Java classes using GA. The whole test suite is a set of generated test cases to cover various coverage criteria at a time. A test case is generated to satisfy the individual coverage criterion [8]

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