Product Comparison: Diffblue Cover vs GitHub Copilot for unit testing

Diffblue Cover

Diffblue Cover vs GitHub Copilot for unit testing

Why settle for AI coding assistant suggestions for unit test writing when you can have an AI Agent take care of the entire test generation and execution end-to-end process for you?

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Specialised for Java

8x Faster Testing

26x Annual Productivity

Anonymous Operation

Specialised for Java

8x Faster Testing

26x Annual Productivity

Anonymous Operation

The Diffblue Cover productivity & efficiency difference

From autonomous bulk unit test suite generation, automatic test maintenance and advanced test management, see how Diffblue Cover’s agentic unit testing solution for Java is built to maximize engineering unit testing efficiency for enterprises tackling complex coding challenges. Diffblue Cover elevates unit test operations beyond the basic test case suggestions that GitHub Copilot provides.

4x faster testing

26x unit test process productivity uplift

4x higher test coverage

As an AI agent, Diffblue Cover offers an autonomous workflow that zeros out the need for developer time spent on writing or editing unit test cases, deeper context-awareness, advanced reasoning and scalable unit testing for both local testing in the IDE and in pre-merge CI pipelines.

Unit Testing Complex Java codebases

See how Diffblue Cover compares to GitHub Copilot

Github Copilot

Diffblue Cover

Primary function.

General interactive coding assistant. Requires human input and iterative interaction.

Specialised autonomous agent for unit test generation and management.

AI category

Assistant

Agent

Unit test generation

Suggests individual tests via chat-based interactions and developer prompts. Method and class level test writing only as prompted.

Autonomously generates an advanced and entire test suites from a single click / command. Can generate expert-level, human-readable tests at method and class level or for an entire project.

Speed

Suggests a test per method or class .

Writes a test every 2 seconds, 250x faster than a developer. Unit testing workflow is 26x faster than GitHub Copilot, but fully unattended.

Execution

May require editing to ensure tests compile and run correctly.

Generates fully compilable and runnable tests.

Accuracy

Tests written require manual verification and rework to eliminate hallucinations, poor and flaky tests.

Tests are guaranteed to compile and run. 100% accurate and deterministic results, every time.

Reliability

Non-deterministic and dependent on prompt input.

Deterministic code generation outcomes.

Edge cases & happy paths

Requires user interaction and prompt engineering. Test output subject to human input and experience.

Automatically generates tests edge cases and for happy path scenarios. Creates multiple tests that vary in the tested scenario, providing more comprehensive coverage of edge cases.

Regression testing

Not suitable for large-scale test generation or regression testing.

Designed to generate regression unit tests at scale.

Test maintenance

Requires manual updates and refinement.

Automatically updates tests after code changes and removes obsolete tests.

Customization

Some configuration of personal preferences available via IDE or through GitHub settings.

Advanced unit test customization to include test frameworks, Java annotations etc. Support for customisation at repo, team and organization level.

Developer UI

IDE chat and GitHub

IDE, CLI and CI pipeline

Test Code Review

Code snippets that need to be manually integrated into existing code.

Automatically integrates code into existing codebase via diff-view for review, acceptance or rejection in the IDE. Review in CLI and CI pipeline.

Machine Learning

Large Language Model. Limited by LLM and user interactions.

Reinforcement Learning. Unsupervised learning capability that adapts, improves and optimizes.

Code analysis

Generates suggestions based on the limited IDE file context, user code selection and prompts.

Analyzes entire codebase and dependancies to generate context-aware tests.

Testability insights

N/A

IDE feedback and project-level insights via Diffblue Cover Reports.

Scalability

Better suited for individual test suggestions.

Designed for large-scale test generation.

Integration

Integrates with IDEs for real-time suggestions.

Integrates with IDE, CI pipelines including GitHub Actions, GitLab and other popular.

Coverage

Coverage depends on developer input and prompts.

Specialist focus on maximizing code coverage.

Learning curve

Requires learning effective prompting techniques.

Minimal, as it operates autonomously.

Deployment

Generally cloud-based or on-prem LLM connection.

Operates locally, on prem, behind your firewall.

IP / Data Security

Code sent to LLM for training unless you opt out.

On-prem only and your code / IP doesn’t leave your environment / premises.

Data retention

Retains your data for 28 days.

Language support

Multiple programming languages.

Specialized for Java.

Testament: The value delivered by unit testing at scale

Goldman Sachs engineering

100% code coverage increase

90%+ time saved writing unit tests

Case Study

Legacy code challenge

750k lines of code securely covered in one legacy app

51% out-of-the box code coverage on first run

Case Study

Want to learn more?

Guide

AI Assistants and AI Agents: What's the Difference?

AI agent looking at a vast amount of code

Blog

GitHub Copilot vs Cover - independent evaluation

Learn more

Diffblue Cover Vs. GitHub Copilot What’s The Difference

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