Stop Wasting Hours Writing Unit Tests: Use GitHub Copilot to Explode Code Coverage Fast

Most development teams say they want better test coverage. Fewer teams say out loud why they do not have it: writing unit tests is slow, repetitive, and usually postponed until after the “real work” is done.

This is exactly where GitHub Copilot changes the economics.

Using GitHub Copilot to generate unit tests for code you have already written is one of the highest-leverage GitHub Copilot workflows available to modern engineering teams. It helps increase line coverage and branch coverage, accelerates creation of a usable test baseline, and gives your team a durable safety net for future changes. More importantly, it turns testing from a backlog item into an integrated part of the delivery process.

The biggest insight is simple: Copilot is especially strong at analyzing existing code and producing test cases that reflect the behavior already encoded in that implementation. When the code already works and has been manually verified, Copilot can help convert that verified behavior into executable tests faster than most engineers will write them from scratch.

Why GitHub Copilot Is So Effective at Generating Tests for Existing Code

When you ask Copilot to help design a new architecture, it has to reason through ambiguity. When you ask it to generate unit tests for code that already exists, much of that ambiguity is gone. The implementation itself becomes the specification, or at least the closest thing many teams have to one.

That matters because most unit tests are built from a small set of repeatable activities:

• Identify inputs
• Infer expected outputs
• Trace conditional branches
• Exercise error handling
• Mock external dependencies
• Validate edge cases
• Confirm side effects

These are pattern-heavy tasks. GitHub Copilot performs well when the problem is pattern-rich and the source material is already present in the repository.

For example, imagine a deployment validation method that checks whether a target environment is allowed, whether required secrets are present, whether the requested image tag meets a promotion policy, and whether the maintenance window is open. A human can write those tests, but it takes time. Copilot can inspect the function and quickly generate tests for:

• valid deployment requests
• missing secrets
• unauthorized environments
• disallowed tags
• maintenance window violations
• combinations of conditions that hit different branches

That is not magic. It is structured pattern extraction from working code.

For platform teams, that is useful because much of your risk lives in logic branches, not just in raw lines executed. Line coverage tells you whether a line ran. Branch coverage tells you whether decision paths were exercised. In reliability-sensitive code, branch coverage is often the more meaningful signal.

Writing Unit Tests Takes Time

The challenge is not that engineers do not understand the value of unit tests. The challenge is that the incentives in most teams push test creation later than it should happen.

Feature work is visible. Tests are invisible until something breaks.

A deployment utility that lacks tests may still “work” in the happy path. A rollback helper may seem fine until one edge case appears in production at 2:00 AM. A policy evaluation function may pass informal review but contain an untested branch that silently skips an enforcement rule. These are not theoretical problems. They are the kinds of defects that create operational incidents, noisy alerts, broken pipelines, and emergency patches.

The tension gets sharper in infrastructure-heavy repos because:

• the code often interacts with external systems
• business logic is mixed with platform concerns
• test suites are frequently uneven across directories and services
• CI pipelines may enforce thresholds, but not quality of test design

So teams end up with one of two bad outcomes.

The first is low coverage and weak change confidence. Engineers hesitate to refactor because nobody is sure what will break.

The second is a false sense of security. The repository reports acceptable coverage numbers, but the tests mostly exercise happy paths and avoid the real failure branches that matter in production.

Copilot does not automatically solve either problem. If used carelessly, it can generate shallow tests that only mirror implementation details without validating meaningful behavior. But when guided well, it becomes a force multiplier that helps teams move from “we should add tests someday” to “every merged change extends or protects our test foundation.”

That shift is operationally significant.

Write Unit Tests Faster with GitHub Copilot

The most useful way to think about Copilot-generated unit tests is this: Copilot is not your test strategy. It is your test acceleration layer.

That distinction matters.

A good test strategy answers questions like:

• What behavior must never regress?
• Which modules are core reliability boundaries?
• Where do we need branch coverage, not just line coverage?
• Which external systems must be mocked versus integration-tested?
• What production incidents should be encoded as regression tests?

Copilot helps execute that strategy at speed.

A useful analogy is infrastructure as code. Terraform does not replace infrastructure design. It makes infrastructure design repeatable, versioned, and automatable. In the same way, Copilot does not replace testing judgment. It makes test creation faster, more consistent, and easier to integrate into the delivery workflow.

For all teams, this has three important effects.

First, it lowers the activation energy for testing. Many engineers skip tests because starting from a blank file feels expensive. Copilot removes the blank page. Once the initial scaffold exists, refining it is much easier than authoring it from nothing.

Second, it helps encode operational knowledge into code. Every outage, every flaky deployment, every broken parser, every bad retry loop can become a prompt for a new AI-generated regression test. That is how reliability matures: pain becomes policy, and policy becomes executable validation.

Third, it makes pull requests safer over time. When each change comes with incremental tests, future changes are less likely to break established behavior. That creates a compounding return. The value is not just the test you generated today. The value is the growing behavioral map your repository accumulates over months.

There is also a leadership angle here.

Teams that treat AI as a code generator only are underusing it. The more strategic use is as a quality amplifier. Test generation is a perfect example because it converts AI from a speed tool into a risk-reduction tool. That is exactly the kind of use case technical leaders should prioritize.

The forward-looking pattern is clear: high-performing teams will not ask whether AI writes code. They will ask whether AI helps enforce engineering discipline at scale. Unit test generation inside the PR process is one of the strongest answers to that question.

Steps to Create a Custom Copilot Agent for Writing Unit Tests

If you want GitHub Copilot to generate better unit tests, do not treat it like a stateless autocomplete tool. Give it durable context about your repository, your engineering standards, and the specific role you want it to play in your pull request workflow.

The most effective way to do that is with two layers:

1. An AGENTS.md file at the root of the repository that gives AI agents project-wide context
2. A specialized unit test agent definition such as .github/agents/unit-test.agent.md that tells Copilot exactly how to behave when generating tests

This pattern matters because AI is only as good as the context it can see. Your repository contains code, but it does not always contain intent. It may show naming patterns and frameworks, but it does not always reveal why certain modules are reliability-sensitive, which mocking conventions your team prefers, or what kinds of regressions have historically caused trouble.

That is where AGENTS.md becomes useful.

Step 1: Create an AGENTS.md at the root of the repository

Start by creating a file named: AGENTS.md

Think of this file as the onboarding guide for AI agents working in your repo. It gives persistent context that helps GitHub Copilot and other AI-driven workflows understand your project beyond just reading source files.

A strong AGENTS.md should explain:

• what the repository does
• who the main audience or operators are
• the primary languages and frameworks
• how the codebase is organized
• how tests are written and run
• what “good” looks like for unit tests
• what kinds of changes are risky
• what patterns AI should follow or avoid

The context added to the AGENTS.md is especially important because many repos mix business logic, platform automation, configuration handling, deployment validation, and operational tooling. GitHub Copilot can inspect the code, but it will not automatically know which modules protect production safety or which code paths deserve extra branch coverage.

Here’s an example of what an AGENTS.md file might look like:

# AGENTS.md

## Repository Purpose
This repository contains internal platform automation for deployment orchestration, policy validation, environment checks, operational tooling, and supporting libraries.

## Audience
The primary users and maintainers are Cloud engineers, DevOps engineers, platform engineers, and SREs.

## Primary Languages and Frameworks
- TypeScript
- Node.js
- Jest for unit testing

## Repository Structure
- `src/` contains core logic
- `libs/` contains reusable internal libraries
- `services/` contains operational services and workflows
- `scripts/` contains automation and maintenance tasks

## Testing Expectations
- All new logic should include unit tests
- Prefer branch coverage, not just line coverage, for validation-heavy code
- Cover happy path, edge cases, invalid input, exceptions, retries, and fallback logic
- Mock all external systems including HTTP calls, cloud SDKs, filesystem operations, timers, and environment state
- Keep tests deterministic and fast

## Reliability-Sensitive Areas
The following areas are especially important:
- deployment validation
- rollback logic
- retry and backoff behavior
- policy enforcement
- parsing and config validation
- secrets and environment checks

## Patterns to Follow
- Use table-driven tests where appropriate
- Prefer behavior-based assertions
- Reuse existing test helpers and fixtures
- Match the style of existing tests in the repo

## Patterns to Avoid
- Do not call live external services in unit tests
- Do not write brittle tests that depend on timing or non-deterministic values
- Do not generate tests that simply restate implementation details without validating outcomes

The goal is not to create a massive document. The goal is to give AI enough context to behave like a helpful contributor instead of a generic code assistant.

Step 2: Ask GitHub Copilot to help create the AGENTS.md for you

One of the most useful AI habits is to explicitly tell it to ask clarifying questions first.

That matters because the repository alone does not contain everything AI needs. It can infer frameworks and patterns, but it cannot always infer team preferences, operational pain points, PR expectations, or the subtle difference between “important code” and “code that tends to wake someone up at 3:00 AM.”

So instead of asking Copilot to generate AGENTS.md immediately, use a prompt like this:

Please analyze my repo and create an AGENTS.md file that will help AI agents. Ask me any clarifying questions first.

This is a strong pattern because it forces a short discovery phase before generation. That lets you provide the context AI cannot get by just reading the repo, such as:

• which modules are the most production-critical
• whether branch coverage matters more than raw line coverage
• how strict your team is about mocking
• whether tests should be colocated or stored in separate directories
• whether the repo is optimized for speed, readability, or strict convention matching
• what recurring bugs or incident patterns should influence test generation

After Copilot asks questions, answer them directly and with as much specificity as possible. Then ask it to draft the file.

A practical follow-up prompt is:

Using the repository analysis and my answers, draft a complete AGENTS.md file for this repo. Make it practical and optimized for AI agents that will help with coding, testing, pull requests, and reliability-focused changes.

Step 3: Review and refine AGENTS.md

Do not just accept the generated file as-is. Review it like you would review any internal engineering standard.

Look for these qualities:

• Is it specific to your repo, or still too generic?
• Does it identify the modules where failures are most expensive?
• Does it tell AI how to approach tests, not just that tests exist?
• Does it reflect your real conventions?
• Does it help an AI understand what production-safe changes look like?

You want AGENTS.md to encode engineering intent, not just repository trivia.

A good rule is this: if a new engineer joined your team and read AGENTS.md, would they understand how to contribute safely? If the answer is yes, it is probably giving AI useful guidance too.

Step 4: Create a specialized GitHub Copilot “Unit Test” Agent

Once AGENTS.md exists, create a dedicated agent definition for writing unit tests. Place it at:

.github/agents/unit-test.agent.md

This file is not the broad repository briefing. It is the role definition for a specialized Copilot agent. If AGENTS.mdanswers “how does this repo work?”, the unit test agent file answers “how should AI behave when asked to generate or update unit tests?”

Your unit test agent should be focused, opinionated, and explicit.

It should tell Copilot to:

• analyze changed files and nearby dependencies
• identify important decision branches
• generate tests for both happy path and failure path behavior
• use the repository’s preferred test framework and mocking style
• create regression tests for bug fixes
• improve branch coverage, not just line coverage
• summarize any branches or behaviors that still need human review

A strong baseline might look like this:

---
name: Unit Test
description: Generates and improves high-value unit tests for this repository, with emphasis on behavior, edge cases, failure paths, and branch coverage.
tools: ['vscode','execute','read','edit','todo']
---

# Unit Test Agent

You are a specialized GitHub Copilot agent for generating and improving unit tests in this repository.

Your primary goal is to help engineers create high-value unit tests that protect behavior, improve line coverage, and increase branch coverage for changed or existing code.

## Code Coverage Target
- Line Coverage: 80% or higher
- Branch Coverage: 80% or higher

## Responsibilities
- Analyze the target file and nearby dependencies
- Identify public methods, conditional branches, exception paths, edge cases, and side effects
- Generate or update unit tests that validate observed behavior
- Prefer behavior-based assertions over implementation-coupled assertions
- Add regression tests for bug fixes
- Match existing repository conventions for test structure, setup, fixtures, mocks, and assertions

## Test Design Expectations
- Cover happy path, edge cases, invalid input, null handling, exceptions, retries, fallback logic, and policy decisions
- Mock all external dependencies including cloud SDKs, HTTP calls, filesystem access, environment variables, shell commands, and timers
- Keep tests deterministic, isolated, and fast
- Use table-driven tests when the code contains multiple validation scenarios

## What to Avoid
- Do not call live services
- Do not invent new product behavior
- Do not generate shallow tests that only restate implementation line-by-line
- Do not make broad refactors unless required to make the code testable

## Output Expectations
When generating tests:
1. Briefly summarize the behavior being tested
2. Explain which branches or scenarios are covered
3. Note any branches that still appear untested or ambiguous
4. Follow the conventions documented in AGENTS.md

Step 5: Ask GitHub Copilot to Create the Unit Test Agent for you

Just like with AGENTS.md, do not ask Copilot to generate the unit test agent blindly. Tell it to ask clarifying questions first.

Use a prompt like this:

Please analyze this repo, then creat a Custom GitHub Copilot Agent (.github/agents/unit-test.agent.md) file that will help me write unit tests. The agent should specify the code coverage target when writing unit tests should be 80% or higher for both Line Coverage and Branch Coverage. Ask me any clarifying questions first.

This gives you a chance to add the context that repo analysis alone often misses, such as:

• whether your team values branch coverage above all else
• how aggressive the agent should be about suggesting testability refactors
• whether regression tests should be mandatory for every bug fix
• which directories or services are most critical
• how much explanation you want in the agent’s output
• whether PR-focused behavior should be emphasized

After Copilot asks questions and you answer them, it will make updates with the additional context you provided.

This sequence usually produces much better results than a one-shot prompt because it combines repository analysis with human context.

Step 6: Put the Custom Unit Test Agent to Work on the Pull Request Workflow

Once both files exist, you can use the custom agent as part of your PR workflow.

Once you submit a Pull Request on your GitHub or GitHub Enterprise repository, you can prompt Copilot to help you use the Unit Test agent to increase your code coverage.

Here’s a sample comment to make, that mentions @copilot, to request GitHub Copilot write unit tests for you:

@copilot Please use the Unit Test agent to write unit tests and increase code coverage for the code additions and updates made within this PR.

With this Pull Request workflow, you can have GitHub Copilot write your Unit Tests to achieve

Tips for Getting Better Results

A few practical habits improve the quality of both AGENTS.md and the custom unit test agent:

• Ask for clarifying questions first. This is one of the highest-value prompt techniques because it lets AI gather intent that is not visible in the codebase.
• Tell Copilot to analyze the repo before writing instructions. You want patterns discovered from the repo, not just generic best practices.
• Be explicit about reliability-critical code. Within every project, not all modules are equally important. You should tell Copilot which code paths protect production safety and which are more important to improve its focus.
• Require branch-oriented thinking. Many generated tests will hit lines but miss meaningful logic forks unless you explicitly ask for branch analysis.
• Have Copilot explain the testing gaps to you. A good Copilot agent should say what it covered and what still appears ambiguous or untested in the report it provides after unit test work is completed.

Always review generated instructions like code. The quality of the agent depends on the quality of its operating instructions.

The larger point is simple: do not just ask Copilot to write tests. Teach it how to think about your repository first. An AGENTS.md file gives it project context. A .github/agents/unit-test.agent.md file gives it a specialized role. Together, those two files turn GitHub Copilot from a generic assistant into a much more effective testing partner inside your pull request process.

Conclusion

The case for using GitHub Copilot to generate unit tests is not just about saving time, even though it absolutely does that. The bigger value is that it helps transform tested behavior into a normal byproduct of delivery.

That matters because reliability is rarely lost in the obvious paths. It is lost in edge cases, conditional branches, fallback logic, parser errors, retry loops, and validation gaps. Those are exactly the areas where unit tests provide leverage, and exactly the areas where Copilot can help teams move faster.

The smartest way to use Copilot here is not as a novelty and not as an autocomplete toy. Use it as a quality engine. Give it repository context. Teach it your standards. Create a custom unit test agent. Put it in the pull request path. Make it help your team increase line coverage, strengthen branch coverage, and build a durable regression safety net around the code that keeps your platforms running.

That is where AI delivers real engineering value: not just in writing more code, but in helping your team break less of it.

Original Article Source: Stop Wasting Hours Writing Unit Tests: Use GitHub Copilot to Explode Code Coverage Fast written by Chris Pietschmann (If you're reading this somewhere other than Build5Nines.com, it was republished without permission.)