Automated Unit Test Case Generation: A Systematic Literature Review

TL;DR

This paper systematically reviews automated unit test case generation, focusing on evolutionary algorithms, their improvements, limitations, and current challenges in the field.

Contribution

It consolidates existing knowledge on evolutionary approaches, hybrid algorithms, and challenges in automated test case generation.

Findings

Hybrid algorithms improve test generation efficiency

Interoperability with mutation testing and neural networks enhances capabilities

Main challenges include readability and mocking issues

Abstract

Software is omnipresent within all factors of society. It is thus important to ensure that software are well tested to mitigate bad user experiences as well as the potential for severe financial and human losses. Software testing is however expensive and absorbs valuable time and resources. As a result, the field of automated software testing has grown of interest to researchers in past decades. In our review of present and past research papers, we have identified an information gap in the areas of improvement for the Genetic Algorithm and Particle Swarm Optimisation. A gap in knowledge in the current challenges that face automated testing has also been identified. We therefore present this systematic literature review in an effort to consolidate existing knowledge in regards to the evolutionary approaches as well as their improvements and resulting limitations. These improvements include hybrid algorithm combinations as well as interoperability with mutation testing and neural networks. We will also explore the main test criterion that are used in these algorithms alongside the challenges currently faced in the field related to readability, mocking and more.