# Instance Space Analysis of Search-Based Software Testing

arXiv: https://arxiv.org/abs/2312.02392 · HTML: https://mlsanity.com/paper/2312.02392/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Dec 2023.

## Built on by (most cited first, 2 of 2)

- [Revealing Domain-Spatiality Patterns for Configuration Tuning: Domain Knowledge Meets Fitness Landscapes](https://mlsanity.com/paper/2603.19897/) (2026)
- [Search-Based Software Engineering and AI Foundation Models: Current Landscape and Future Roadmap](https://mlsanity.com/paper/2505.19625/) (2025)

## Abstract (arXiv)

Search-based software testing (SBST) is now a mature area, with numerous techniques developed to tackle the challenging task of software testing. SBST techniques have shown promising results and have been successfully applied in the industry to automatically generate test cases for large and complex software systems. Their effectiveness, however, is problem-dependent. In this paper, we revisit the problem of objective performance evaluation of SBST techniques considering recent methodological advances -- in the form of Instance Space Analysis (ISA) -- enabling the strengths and weaknesses of SBST techniques to be visualized and assessed across the broadest possible space of problem instances (software classes) from common benchmark datasets. We identify features of SBST problems that explain why a particular instance is hard for an SBST technique, reveal areas of hard and easy problems in the instance space of existing benchmark datasets, and identify the strengths and weaknesses of state-of-the-art SBST techniques. In addition, we examine the diversity and quality of common benchmark datasets used in experimental evaluations.