CodeBeaver

Product Introduction

1. CodeBeaver is an AI-powered unit test automation platform that integrates with GitHub, GitLab, and Bitbucket to analyze code changes, generate tests, and submit pull requests (PRs) with test updates. It operates continuously with every code push, dynamically expanding test coverage while identifying bugs through automated test execution. The system eliminates manual test writing by leveraging large language models (LLMs) to understand code intent and generate context-aware tests.
2. The core value lies in reducing development cycle time by automating repetitive test creation and maintenance, allowing teams to focus on feature development. CodeBeaver ensures code reliability by running tests in isolated environments and providing actionable bug fixes directly in PR comments. Its CI/CD-aware architecture auto-updates outdated tests to reflect code changes, preventing technical debt accumulation.

Main Features

1. CodeBeaver automatically writes new unit tests for code changes and submits them via PRs, formatted to match project standards. It analyzes function behavior, dependencies, and edge cases using LLMs, generating tests for both success and failure scenarios. Tests are executed in isolated environments, with results reported alongside coverage metrics.
2. The platform updates existing test files to cover additional edge cases by cross-referencing code modifications with test suites. It identifies under-tested logic and writes targeted assertions, improving coverage iteratively without manual intervention. This ensures tests evolve alongside the codebase, maintaining relevance.
3. CodeBeaver detects test failures caused by bugs and provides PR comments explaining root causes, including code snippets and fixes. It distinguishes between genuine issues and outdated tests by analyzing code intent, reducing false positives. For obsolete tests, it auto-generates updates to align with current logic.

Problems Solved

1. Manual unit test creation and maintenance consume significant developer time, often delaying releases and increasing technical debt. CodeBeaver automates these processes, reducing test-related workload by 85% in benchmarked teams. It also prevents obsolete tests from causing false failures during CI/CD runs.
2. The tool targets software teams using GitHub, GitLab, or Bitbucket who prioritize rapid feature delivery without compromising quality. It is particularly effective for projects with frequent code changes or legacy systems lacking adequate test coverage.
3. Use cases include automating test generation for new features, expanding coverage in untested legacy codebases, and preventing regressions in continuous deployment pipelines. It also standardizes testing in open-source projects with high contributor turnover.

Unique Advantages

1. Unlike traditional CI tools that only execute predefined tests, CodeBeaver writes and adapts tests using semantic code analysis. Competitors like GitHub Co-Pilot lack integrated test execution and failure analysis, requiring manual intervention.
2. The platform combines LLM-based test generation with automated root-cause analysis, a dual capability absent in other tools. It introduces self-healing tests that update when code changes, critical for agile development.
3. CodeBeaver supports all programming languages and testing frameworks via adaptive LLMs, with pre-optimized configurations for Jest, pytest, and JUnit. Enterprise-grade encryption and SOC 2 compliance ensure data privacy, while a 14-day free trial lowers adoption barriers.

Frequently Asked Questions (FAQ)

1. Won’t CodeBeaver commit tests that pass for buggy code?
   CodeBeaver analyzes code structure and comments to validate intended behavior, reducing false positives. Tests are designed to fail if code outputs deviate from documented expectations, with discrepancies flagged in PR reviews.
2. How is code privacy handled?
   All code is processed in encrypted memory and transiently stored only during analysis. Data retention policies ensure no user code persists post-processing, adhering to SOC 2 compliance standards.
3. What languages and frameworks are supported?
   The platform is language-agnostic, leveraging LLMs trained on multiple paradigms. Pre-configured support exists for Jest (TypeScript/JavaScript), pytest/unittest (Python), and JUnit (Java), with customization options for proprietary frameworks.