Testing with AI Agents: An Empirical Study of Test Generation Frequency, Quality, and Coverage

TL;DR

This empirical study analyzes how AI agents generate tests in real-world software development, revealing their frequency, structural characteristics, and impact on code coverage, demonstrating AI's effectiveness in augmenting testing practices.

Contribution

The paper provides the first comprehensive empirical analysis of AI-generated tests in real-world repositories, highlighting their structural features and coverage contributions.

Findings

AI authored 16.4% of test commits in repositories

AI-generated tests have longer code and more assertions

AI tests achieve coverage gains comparable to human tests

Abstract

Agent-based coding tools have transformed software development practices. Unlike prompt-based approaches that require developers to manually integrate generated code, these agent-based tools autonomously interact with repositories to create, modify, and execute code, including test generation. While many developers have adopted agent-based coding tools, little is known about how these tools generate tests in real-world development scenarios or how AI-generated tests compare to human-written ones. This study presents an empirical analysis of test generation by agent-based coding tools using the AIDev dataset. We extracted 2,232 commits containing test-related changes and investigated three aspects: the frequency of test additions, the structural characteristics of the generated tests, and their impact on code coverage. Our findings reveal that (i) AI authored 16.4% of all commits adding tests in real-world repositories, (ii) AI-generated test methods exhibit distinct structural patterns, featuring longer code and a higher density of assertions while maintaining lower cyclomatic complexity through linear logic, and (iii) AI-generated tests contribute to code coverage comparable to human-written tests, frequently achieving positive coverage gains across several projects.