Bitbucket + CloudAEye Code Review

Dec 11, 2025. 10 min

CloudAEye Code Review Now Supports Bitbucket

Introduction

CloudAEye is expanding its ecosystem. Today, we are excited to announce full support for Bitbucket, bringing our intelligent Code Review capabilities to thousands of teams that rely on Atlassian's developer platform. With this release, CloudAEye now embeds directly into GitHub, Bitbucket, and VS Code, helping engineers ship high-quality software up to four times faster by automating critical post-coding workflows.

Startups and growing engineering organizations face increasing pressure to deliver reliable software at scale. CloudAEye removes friction from dev-test cycles, eliminates slow review bottlenecks, and surfaces the insights teams need to build with confidence. Recognized by AngelList (Wellfound) as a Top 10 AI Startup and featured in TechCrunch's AI Startup Battlefield, CloudAEye is redefining how modern software teams build, test, and ship.

Bringing CloudAEye Capabilities to Bitbucket

With Bitbucket support, CloudAEye integrates directly into your pull requests, giving reviewers deep context, intelligent automation, and actionable insights without leaving Atlassian's ecosystem.

Key capabilities now available for Bitbucket teams include:

Human-Like Code Review (/review)

CloudAEye delivers high-quality, architect-level reviews that detect issues early. Our system understands agentic AI patterns, CNCF stacks, and widely used open-source libraries, producing feedback that aligns with how real engineers reason about code.

Full Codebase Context

Instead of reviewing diffs in isolation, CloudAEye builds a detailed graph of your entire codebase from dependencies to directory structure to internal APIs. This holistic understanding results in more accurate findings, fewer defects, and reviews grounded in real system behavior.

Tech Stack Awareness

CloudAEye learns your frameworks, languages, infrastructure, and architectural conventions. Every recommendation is tailored to your actual implementation patterns, not generic best practices.

Advanced Security Review for LLM, Agentic, and MCP-Based Applications

CloudAEye provides a comprehensive, modern security assessment that extends well beyond traditional static analysis. In addition to reviewing code against industry standards such as the OWASP Top 10 and the Agentic Security Initiative (ASI), CloudAEye evaluates repositories for vulnerabilities unique to LLM-powered applications, agentic systems, and MCP-based tooling. This ensures teams building AI-driven applications maintain strong security controls across their entire software and AI infrastructure.

1. LLM and GenAI Application Security: CloudAEye evaluates code and configuration to detect vulnerabilities specific to large language model applications and GenAI-powered systems, including: Prompt Injection, Sensitive Information Disclosure, System Prompt Leakage, etc.
2. Agentic Security: As engineering teams adopt agent-driven workflows, CloudAEye provides built-in analysis and pattern detection to safeguard multi-agent pipelines and autonomous planning systems including: Memory Poisoning, Tool Misuse, Privilege Compromise, etc.
3. MCP Server Security: For teams adopting the Model Context Protocol (MCP) as an operational layer for tools and external resources, CloudAEye includes dedicated security checks for MCP-enabled systems including: Tool Poisoning, Rug Pull Attacks, Prompt Injection via MCP Inputs, etc.

Fundamentals

CloudAEye code review ensures that code is reliable and maintainable. Here are few examples:

1. No Secrets in Code: Secrets, API keys, or credentials are not exposed.
2. No Duplicate Code: Redundant or duplicated code (semantic or syntactic) has been removed.
3. Code Clarity: Code is easy to read and understand.
4. Naming Consistency: Functions, variables, and files use clear and descriptive names.
5. Error Handling: Errors and exceptional cases are handled safely and predictably.
6. Input Validation: Inputs are validated to prevent incorrect or unsafe usage.
7. Edge Case Handling: Data and variable edge cases have been considered and tested.
8. Function Signatures: Any updated function signatures are correct and consistently applied.
9. Runtime Safety: Code is free from algorithmic or runtime errors.
10. Injection Protection: Code is safeguarded against SQL/NoSQL/command injection.
11. Sensitive Data Handling: PII or sensitive data is not leaked in logs or request parameters.
12. Authentication & Authorization: Access controls are implemented correctly without bypass paths.
13. Safe Deserialization: Deserialization logic avoids insecure patterns.
14. Security Misconfiguration: Lazy or overly permissive security settings that make your app easier to attack are not used.
15. XML External Entity (XXE): Reading XML files without blocking external references that attackers can no longer use to steal sensitive files or make unauthorized requests.

Custom Rules

Teams can define their own review standards in plain English. CloudAEye automatically interprets, applies, and scopes each rule, enabling fully personalized reviews that reflect your engineering culture.

Continuous Learning and Intelligent Rule Generation

CloudAEye not only adapts to your team's preferences, it actively learns from your organization's review history. By analyzing previously reviewed pull requests, CloudAEye identifies patterns in your team's feedback, style, architectural preferences, and recurring concerns. It then converts these insights into actionable review rules, ensuring that your engineering standards are consistently applied across new code changes.

In addition to historical learning, CloudAEye responds to real-time reviewer input. When an engineer reacts to a review comment, affirming it, dismissing it, or providing direct feedback, CloudAEye interprets that signal and can automatically generate or refine rules based on the reviewer's intent. This transforms incidental reviewer feedback into durable organizational knowledge.

By observing which comments your team accepts or rejects, the system adapts its guidance to match your standards and coding conventions. Over time, CloudAEye evolves into a highly personalized review system that mirrors your team's expectations and coding style, with rules grounded in actual engineering behavior rather than static, generic guidelines.

Integrated Linters and Best-Practice Setup

CloudAEye integrates seamlessly with your existing linters, unifying stylistic, structural, and maintainability checks into a single, coherent workflow. Teams can plug in popular tools such as ESLint, Pylint, Flake8, or GolangCI-Lint and CloudAEye automatically incorporates their findings into each review.

Beyond simple integration, CloudAEye supports industry best practices for linter configuration. It guides teams in setting up consistent rule sets, enforcing language-specific conventions, and aligning checks with widely accepted standards across CNCF and open-source ecosystems. This includes baseline configurations (such as ESLint's recommended rules), layered rule profiles for large monorepos, and harmonized settings for multi-language environments.

By centralizing linter output, normalizing rule interpretations, and aligning configurations with modern best practices, CloudAEye ensures your codebase remains clean, readable, and sustainable while reducing the manual overhead typically associated with maintaining linter configurations across multiple repositories.

Automated Workflows

Automatically trigger reviews based on labels, authors, branches, or other filters ensuring every PR receives consistent scrutiny without manual oversight.

Tracking Issue Creation

Convert any review comment into a Jir or GitHub issue instantly. CloudAEye handles the formatting, linking, and cross-repo references so teams never lose track of follow-ups.

Suggested Fixes (/implement)

Engineers receive precise code modifications for identified issues, reducing cognitive overhead and accelerating PR turnaround.

PR Description Generation (/describe)

CloudAEye automatically drafts clear, detailed PR descriptions based on the actual code changes.

Automated Test Generation (/test)

Generate and update unit tests with every change. CloudAEye covers edge, error, and negative paths to help teams reach full test coverage.

Chat With Code (/ask)

Query and navigate massive enterprise codebases spanning hundreds of repositories with natural questions instead of manual searching.

Issue Explainer (/explain)

Convert Jira or GitHub Issues into step-by-step implementation guidance, powered by your codebase context.

Code Documentation (/add_docs)

Generate docstrings, improve code readability, and maintain consistent documentation across the repository.

Why This Matters for Bitbucket Teams

Engineering organizations using Bitbucket can now access the same depth of intelligence and automation previously available to GitHub users. This integration:

1. Accelerates review cycles and improves PR quality
2. Reduces context switching across tooling
3. Standardizes development workflows across teams
4. Strengthens security and code hygiene
5. Enhances team velocity without increasing headcount

CloudAEye gives Bitbucket teams a modern, AI-driven development experience without the overhead of adopting new processes or rewriting existing pipelines.

Getting Started

CloudAEye for Bitbucket is available today. Teams can connect their Bitbucket workspace, enable automated reviews, and begin receiving high-context insights within minutes.

If your team is ready to elevate code quality, streamline dev-test workflows, and ship faster with confidence, CloudAEye is ready to help.

Get started today!

References

1. Use Code Review at CloudAEye
2. Docs: Getting Started with Code Review on Bitbucket
3. Video: Overview of Code Review