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Automated Unit Test Case Generation: A Systematic Literature Review

Jason Wang, Basem Suleiman, Muhammad Johan Alibasa

TL;DR

This systematic literature review analyzes Automated Unit Test Generation (AUTG) with a focus on white-box SBST using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). It aggregates evidence on how GA and PSO operate, surveys improvements including hybrids and neural network augmentations, and highlights persistent challenges such as environmental mocking, readability, and dynamic language support. The study demonstrates that SBST generally outperforms Random Testing in coverage and fault detection, but adoption is hindered by practical issues, notably in real-world environments and in producing meaningful, maintainable test cases. It underscores the need for standardized benchmarks, broader methodological scope beyond GA/PSO, and ongoing research into overcoming local optima and genetic drift to enable wider industry uptake. Overall, the work provides a consolidated view of advances and gaps, guiding future development towards more scalable, readable, and environment-aware AUTG methodologies.

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.

Automated Unit Test Case Generation: A Systematic Literature Review

TL;DR

This systematic literature review analyzes Automated Unit Test Generation (AUTG) with a focus on white-box SBST using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). It aggregates evidence on how GA and PSO operate, surveys improvements including hybrids and neural network augmentations, and highlights persistent challenges such as environmental mocking, readability, and dynamic language support. The study demonstrates that SBST generally outperforms Random Testing in coverage and fault detection, but adoption is hindered by practical issues, notably in real-world environments and in producing meaningful, maintainable test cases. It underscores the need for standardized benchmarks, broader methodological scope beyond GA/PSO, and ongoing research into overcoming local optima and genetic drift to enable wider industry uptake. Overall, the work provides a consolidated view of advances and gaps, guiding future development towards more scalable, readable, and environment-aware AUTG methodologies.

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.
Paper Structure (37 sections, 24 figures, 3 tables)

This paper contains 37 sections, 24 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Past Literature Reviews
  4. Methodology
  5. Filtering Methodology
  6. Stage 1 Filtering - Initial Selection
  7. Stage 2 Filtering - Abstract Filtering
  8. Stage 3 Filtering - Consumption and Analysis
  9. Testing Criterion
  10. Criterion
  11. Statement Coverage
  12. Branch Coverage
  13. Path Coverage
  14. Mutation Score
  15. MC/DC
  16. ...and 22 more sections

Figures (24)

  • Figure 1: PRISMA Diagram of Selection and Filtering Process
  • Figure 2: checkSign function showcasing an example decision tree. Zhang-Smartunit
  • Figure 3: Different operators used for mutation analysis Mishra-Genetic-MT
  • Figure 4: More Mutation Operators Rani-Elitist-GA
  • Figure 5: Source code of GCD program Mishra-Genetic-MT
  • ...and 19 more figures