In the fast-evolving world of enterprise Java development, efficiency is king. The evolution of artificial intelligence(AI) in software development has introduced two distinct AI product categories: AI Assistants and AI Agents. While both have their merits, AI Agents are emerging as the superior choice for software testing due to their autonomy, complexity handling, and ability to optimize workflow efficiency.
The Challenge: Scaling unit testing
Ensuring code quality without compromising speed of development and delivery is a constant challenge. Unit testing is the best method of continuous testing and code validation as early as possible in the SDLC, but keeping up with the demands of unit testing, especially in large, complex Java projects can be a sink hole of developer time.
- Manual Effort: Writing tests by hand is a time sink, diverting valuable developer resources from feature development and innovation.
- Developers using AI Assistants to suggest and write sample tests: this method requires a developer to prompt and tell an assistant what’s needed. It’s a time consuming, iterative process that can result in hallucinations, flakes and tests that don’t compile and run.
- Coverage Gaps: It’s easy when writing tests manually or when using an AI assistant, to miss edge cases and complex logic, leading to incomplete test coverage and potential bugs slipping through the cracks.
- Maintenance Overhead: As code evolves, tests need to be updated, adding to the maintenance burden.
In the context of these challenges, we wanted to see which type of AI for code product is most helpful and reliable when used by developers to:
- speed up unit testing (a critical but time-consuming process)
- reduce the amount of manual effort required
- achieve the most accurate and reliable test outputs
So, we decided to conduct a controlled, scientific study pitching the two leading AI solutions for unit testing Java head-to-head: GitHub Copilot (AI assistant) vs. Diffblue Cover (autonomous AI agent) to compare their testing performance.
How we tested:
We created a controlled environment, using complex open-source Java projects. We tasked both tools with unit testing 20,000 lines of code. We tracked everything: coverage, speed, developer effort, and test quality.
This benchmark study focused on assessing the efficiency, coverage, code quality and usability of both products when unit testing complex Java code, and highlighted results based on measures that are particularly important to end users and larger engineering organizations.
The Results (Spoiler Alert):
The autonomous AI agent wins! Diffblue Cover blew Copilot out of the water:
- 4x Faster Unit Test Generation
- 10x More Tests Written
- 4x Higher Test Coverage
- 26x Increase in Unit Test Process Productivity
Diffblue Cover’s autonomous approach meant less developer intervention and way more tests generated, leading to significantly higher test coverage and productivity. Copilot needed constant babysitting.
Get the full report
But don’t just take our word for it. See for yourself. If you’re interested in the study results, download the full benchmark study to see all the juicy details, charts, and analysis. Find out exactly how an agentic AI unit test solution can revolutionize your code testing process!
