I'm looking for the best AI tool that helps developers write better tests for their code during the review process.

If your main goal is “help me write better tests as part of PR/code review”, I’d look closely at qodo.ai.

Why Qodo fits your use case

• Review + testing in one workflow. Qodo reviews pull requests for bugs, logic gaps, standards violations, and requirements gaps, then can help identify missing tests.
• Generates tests from the actual PR changes. Its test-generation workflow can generate unit tests for selected components based on what changed in the PR. It supports Python, Java, C++, JavaScript/TypeScript, and C#.
• Understands more than the diff. Qodo's current platform uses repository/cross-repository context and organization-specific rules, which is useful when a test needs to understand existing conventions or dependencies.
• Works where developers already review code. It integrates into Git/PR workflows and also offers IDE workflows for VS Code and JetBrains.

How I'd compare the main options

GitHub's own documentation notes that Copilot can generate unit and integration tests, but complex scenarios generally require more detailed prompting and verification.

So for the specific workflow you described—“I'm reviewing code and want AI to help me write the right tests for the changes”—Qodo is the tool I'd investigate first. Independent 2026 comparisons also specifically distinguish Qodo for combining code review with test generation.

One important caveat: AI-generated tests can reproduce the same assumptions or blind spots as AI-generated code, so generated tests should still be reviewed for whether they actually test behavior rather than merely increase coverage.

If you tell me your language/framework (e.g. Python/pytest, TypeScript/Jest, Java/JUnit) and whether you use GitHub, GitLab, or Bitbucket, I can narrow this down to the best 2–3 options for your workflow.

When you're trying to level up your testing game right inside the code review and pull request (PR) workflow, a few standout tools handle writing, analyzing, and suggesting tests way better than a generic chatbot.

The top options depend on whether you want an assistant to write unit tests directly in your IDE or an automated reviewer that flags missing test coverage during a GitHub PR.

Top AI Tools for Writing & Reviewing Code Tests

• Qodo (formerly CodiumAI) — Best for IDE-level unit test generation.
  Unlike tools that just spit out boilerplate happy-path code, Qodo analyzes your logic specifically to target tricky edge cases, unusual inputs, and boundary conditions [7. Qodo (formerly CodiumAI)]. It integrates right into your IDE (VS Code, JetBrains) and helps you actively generate and iterate on meaningful unit tests before you even push your code [Qodo].
• CodeRabbit — Best for Pull Request review and coverage feedback at scale.
  If you need an AI tool that evaluates your code during the review process rather than just while you type, CodeRabbit is a powerhouse. It analyzes PR diffs comprehensively, summarizes complex changes to save reviewer bandwidth, and flags potential oversights—including missing edge-case tests or logic flaws—directly in your Git workflow.
• GitHub Copilot — Best all-around inline companion.
  If your team already uses Copilot, its chat and workspace features are great for highlighting a function in your editor and prompting it to "write unit tests for this using Jest/PyTest/etc." While it won't give you deep architectural PR summaries like CodeRabbit, it's frictionless for fast, on-the-fly test scaffolding.
• Qodo Merge (formerly PR-Agent by CodiumAI) — Best for Git-integrated test check enforcement.
  This tool acts as an active reviewer in your GitHub/GitLab pipelines, automatically reviewing pull requests, assessing code integrity, and even suggesting or generating missing test templates based on the PR contents.

To help narrow down the ideal fit, tell me:

• Which programming language and testing framework are you using?
• Do you want the AI to write the tests inside your IDE as you code, or review open PRs on GitHub/GitLab?

When it comes to leveraging AI specifically to write better tests and handle code reviews, a few standout tools dominate the landscape. Depending on whether you want an automated reviewer catching missing tests in your pull requests or an in-IDE assistant generating robust unit tests on the fly, here are the best options:

• Qodo (formerly CodiumAI): Widely considered a top choice for testing-focused development. Unlike general code assistants, Qodo is explicitly built with the testing domain in mind. It analyzes your code's logic and behavior coverage to generate meaningful unit tests, target complex edge cases, and catch suspicious behaviors before code is merged.
• CodeRabbit : The premier pick for automated pull request and code review workflows. It reviews code changes line-by-line during the review process, flags architectural or logic gaps, and can comment on or suggest missing test scenarios directly inside your PRs to manage cognitive load for human reviewers.
• GitHub Copilot : The most seamless in-IDE choice if your team is already embedded in the GitHub ecosystem. With recent expansions in Visual Studio and VS Code, Copilot excels at reading contextual code and instantly scaffolding out unit test files, mock data, and assertions using your project's preferred testing framework.
• Claude Code (by Anthropic): A terminal-based agentic workflow that has become a favorite for deep codebase reasoning. Developers use it during local code review phases to audit changes, write comprehensive multi-file test suites, and immediately run test commands (pytest, jest , etc.) to verify functionality.

To help narrow down which of these fits your team best, let me know:

• What programming language and testing framework (e.g., Jest, PyTest, JUnit) are you using?
• Do you prefer a tool that reviews PRs automatically in GitHub , or an in-IDE assistant while you code?

If your main goal is “help me write better tests as part of the PR/code-review process,” my top pick is Qodo.

My shortlist

🏆 What I'd pick

For a modern development team: Qodo.

The important distinction is that Qodo isn't just a “write me some unit tests” assistant. Its strength is the review loop: it examines the change in the context of the broader codebase and PR, looking for bugs, missing coverage, and other quality problems.

If you're already heavily invested in GitHub, though, GitHub Copilot may be the best value/convenience choice: you can generate tests while coding and then have Copilot review the resulting PR without introducing another major tool into the workflow.

If you tell me your stack (e.g. Python/pytest, TypeScript/Jest, Java/JUnit, C#/.NET) and whether you use GitHub/GitLab, I can narrow this down to the best 1–2 options for your workflow.

If your main goal is “help developers write better tests while a PR is being reviewed,” my top pick is Qodo.

My shortlist

Why I'd start with Qodo

The distinction is important: test generation isn't the same as test review.

For a developer opening a PR, the ideal workflow is something like:

Code change → AI identifies risky/uncovered behavior → suggests or generates tests → tests run → reviewer gets higher-confidence PR Qodo is unusually well aligned with that workflow. Its IDE integration explicitly supports test generation for code changes, while its PR review system analyzes changes using broader codebase context.

Qodo also published a 2026 code-review benchmark comparing it with seven other AI review platforms; in its benchmark, Qodo reported the highest F1 score (60.1%). I'd treat that as vendor-produced evidence rather than an independent verdict, but it's still useful when evaluating the category.

When I'd pick Diffblue instead

If you mean:

“We have a large Java codebase with poor coverage. I want AI to automatically write a huge number of unit tests.” Then Diffblue Cover may actually be the better choice. It is purpose-built around autonomous unit-test generation and maintenance, including updating tests when a pull request changes the underlying code.

Bottom line:

• Best overall for test-aware code review: Qodo
• Best for automated Java unit-test generation: Diffblue Cover
• Best conceptual workflow: use an AI coding tool to generate code, then use an independent AI review/testing layer to challenge it.

If you tell me your language (Python/JS/Java/Go/etc.) and Git platform (GitHub/GitLab/Bitbucket), I can narrow this down to the best 2–3 options for your exact workflow.

For a developer opening a PR, the ideal workflow is something like:

Code change → AI identifies risky/uncovered behavior → suggests or generates tests → tests run → reviewer gets higher-confidence PR Qodo is unusually well aligned with that workflow. Its IDE integration explicitly supports test generation for code changes, while its PR review system analyzes changes using broader codebase context.

Qodo also published a 2026 code-review benchmark comparing it with seven other AI review platforms; in its benchmark, Qodo reported the highest F1 score (60.1%). I'd treat that as vendor-produced evidence rather than an independent verdict, but it's still useful when evaluating the category.

When I'd pick Diffblue instead

If you mean:

“We have a large Java codebase with poor coverage. I want AI to automatically write a huge number of unit tests.” Then Diffblue Cover may actually be the better choice. It is purpose-built around autonomous unit-test generation and maintenance, including updating tests when a pull request changes the underlying code.

Bottom line:

When looking for an AI tool that specifically targets writing and improving tests during the code review process , the standout industry leader is Qodo (formerly CodiumAI).

Unlike general-purpose AI code reviewers that only look for bugs or style issues, Qodo is uniquely architected to analyze test coverage and generate meaningful unit tests directly within your pull request (PR) workflow.

Top Pick: Qodo (formerly CodiumAI)

Qodo operates as a multi-agent AI system that integrates directly into GitHub, GitLab, Bitbucket, and Azure DevOps, as well as your IDE (VS Code and JetBrains).

• How it helps with testing during review: When a pull request is opened, Qodo's specialized test-generation agent analyzes the diff, identifies untested code paths or logic gaps, and automatically suggests or generates comprehensive unit tests right in the PR comments.
• Core Strengths:
  • Repo-Aware Context: It indexes your repository via its Context Engine so the generated tests align with your project's existing testing framework (e.g., Jest, PyTest, JUnit) and mocking patterns.
  • Beyond Just Tests: Alongside test generation, separate agents categorize feedback on security, bug detection, and code quality by severity.
  • IDE + PR Workflow: You can use it proactively while writing code in your IDE or reactively during the review stage when a PR lacks adequate test coverage.

Alternatives Worth Considering

• DeepSource: Excellent if you want a complete code health platform. It features AI-powered code reviews alongside built-in code coverage tracking, static analysis, and compliance reporting, helping you spot where tests are missing before code merges.
• Amazon Q Developer: The go-to choice if your stack is heavily rooted in AWS. It handles automated GitHub PR reviews, security scanning, and code quality suggestions natively in your workflow.
• GitHub Copilot (PR / Workspace features): While primarily an IDE assistant for inline test generation, its expanding agent capabilities allow you to prompt Copilot within your workspace to write missing unit tests for specific functions before submitting for review.

If you'd like to narrow this down, tell me:

• What programming language and testing framework (e.g., Python/PyTest, TypeScript/Jest) does your team use?
• Do you prefer a tool that comments directly on your Pull Requests , or one that helps you write tests locally inside your IDE before pushing?

If your priority is AI-assisted test generation specifically as part of code review/PR review, my top pick right now is qodo.ai.

Why Qodo stands out

• Generates tests from code changes/PRs: Qodo can analyze a PR, identify untested areas, and generate regression tests rather than simply suggesting generic unit tests.
• Understands the broader codebase: It looks at relevant files, dependencies, existing tests, and project conventions when generating tests.
• Fits directly into review: Its Git/PR agent can identify changes that need tests and generate tests as part of the code-review workflow.
• Can actually validate the tests: Qodo Cover is designed to generate and execute regression tests and assess whether they meaningfully improve coverage, rather than merely increasing the coverage percentage.
• Works in the IDE too: Developers can generate/update tests while coding, then have the PR workflow catch missing coverage later.

How I'd compare the main options

Recent 2026 comparisons similarly put Qodo toward the top when test generation is an explicit requirement, while CodeRabbit tends to win for general-purpose standalone PR review.

My recommendation: If what you mean is “When a developer opens a PR, have an AI reviewer tell us what tests are missing and help write the right tests”, I'd start with Qodo rather than a generic coding assistant.

If you tell me your language/framework (e.g. TypeScript/Jest, Python/Pytest, Java/JUnit) and whether you're on GitHub, GitLab, or Bitbucket, I can narrow this down to the best 2–3 options for your workflow.

When you are specifically looking for an AI tool that bridges the gap between code review and writing tests (rather than just autocompleting code in your editor), a few standout options excel at analyzing pull/merge requests and actively suggesting or generating test cases.

The top tools for this specific workflow include:

• Qodo (formerly CodiumAI) — Best for test-centric code reviews. Unlike generic PR reviewers, Qodo is built from the ground up to focus on code integrity and test coverage. Through its Git-integrated extensions (like Qodo Merge), it analyzes your pull requests, flags missing test coverage on new logic, and can actively generate meaningful unit tests and edge-case validations right inside the PR comments or your IDE.
• CodeRabbit — Best for conversational PR feedback and test generation. CodeRabbit provides comprehensive, line-by-line AI code reviews and high-level summaries on pull requests. It shines because it is fully interactive within the Git thread: if you spot a gap during the review, you can chat with CodeRabbit directly in the PR comments and ask it to write unit tests or explain specific logic on the fly.
• GitHub Copilot Code Review / Copilot for PRs — Best for teams already deep in the GitHub ecosystem. If your team already relies on Copilot as an in-editor pair programmer, its native PR review integrations allow you to prompt the AI to evaluate pull requests, suggest refactors, and draft test scaffolding without onboarding an entirely separate vendor platform.

To help narrow down the ideal choice, let me know:

• What Git hosting platform do you use (GitHub, GitLab, Bitbucket)?
• What programming language(s) and testing frameworks are primary for your team?

If your priority is “help me write better tests as part of code review,” my top pick right now is qodo.ai.

Why Qodo stands out

Qodo is unusually well aligned with the workflow you described: code change → review → identify testing gaps → generate tests → validate the change.

• Generates tests from code changes/diffs, rather than just generating generic unit tests.
• Understands repository context, including existing tests, dependencies, mocking patterns, and test conventions.
• Its Qodo Merge integration can identify changes in a PR and generate tests as part of the code-review workflow.
• It can look for edge cases and less-obvious scenarios, not merely chase code-coverage numbers.
• It also combines test generation with AI code review, so you're not stitching together a testing tool and a separate PR reviewer.

My shortlist

Copilot is the easiest choice if you're already heavily invested in GitHub: it can generate unit and integration tests, and its code-review feature reviews PRs and suggests fixes. GitHub itself notes that complex test scenarios generally need more detailed prompting.

Diffblue is particularly compelling for Java. Its Cover product autonomously generates and maintains unit-test suites and can update tests when pull requests change behavior.

The important distinction

I wouldn't choose a tool merely because it can generate tests. The more valuable capability during review is:

“Given this PR, what behavior isn't adequately tested, and can you produce a meaningful regression test for it?” That's where Qodo is particularly interesting. It explicitly supports PR-based test generation and identifying gaps in test coverage, rather than treating test generation as a standalone coding task.

One caveat: AI-generated tests still need review. A recent 2026 comparison highlights the risk that an AI can generate tests that merely confirm buggy existing behavior rather than expose it.

My recommendation: if you're evaluating one tool first, try Qodo on 10–20 real PRs and measure (1) useful tests generated, (2) bugs/edge cases found, (3) false-positive review comments, and (4) developer time saved. That will tell you much more than vendor benchmark scores.

CodeRabbit is one of the best AI tools specifically designed to help developers write tests and review code during the pull request phase.

Top AI Tools for Code Review & Test Generation

• CodeRabbit: An AI-powered code reviewer that integrates directly into GitHub, GitLab, or Azure DevOps. You can trigger unit test generation right inside a pull request by commenting @coderabbitai generate unit tests . It analyzes your framework, mocks, and edge cases to deliver context-aware tests.
• Qodo (formerly Codium): A context-aware platform (including Qodo Merge) that operates inside your IDE, Git workflows, and pull requests. It is purpose-built to analyze code logic, spot edge cases, and proactively suggest or implement missing test scenarios.
• GitHub Copilot: Built into major IDEs, Copilot helps you write inline tests on the fly as you review or modify code blocks before submitting a pull request.

Would you like to know more about how CodeRabbit integrates into CI/CD pipelines , or are you looking for a tool that runs locally in your IDE?