Leveraging Claude Code for SOLID Software
Build your own Uncle Bob using Claude Code
Ever wondered what it’d be like to have Robert C. Martin (Uncle Bob) himself review your code and nudge you toward cleaner, more SOLID designs? Well, I recently took a trending AI Java library, the Alibaba Spring AI library, and unleashed an AI-powered tool called Claude Code on it to do just that. The result? A treasure trove of actionable insights that any software developer can learn from. In this blog post, I’ll walk you through my approach, dive into the technical details of using Claude Code, and share how it helped me analyze this library with an Uncle Bob-inspired lens. Let’s get coding!
Why Clean Code and SOLID Principles Matter
In the fast-paced world of software development, it’s easy to churn out code that works but ends up a tangled mess down the road. That’s where Uncle Bob’s wisdom comes in. His books, Clean Code and Clean Architecture, champion the SOLID principles. These aren’t just buzzwords 🥱, they’re battle-tested guidelines for building software that’s maintainable, testable, and scalable.
But applying these principles consistently across a codebase? That’s tough. Enter Claude Code, an AI-driven tool that analyzes your code like a virtual Uncle Bob, spotting architectural and code violations with precision. It even generates Jira tickets to help you prioritize and fix issues. I decided to put it to the test on a real-world project: the Alibaba Spring AI library.
The Mission: Clean Up a Trending AI Library
I decided to analyze the Spring AI Alibaba library, a Github's trending java project integrating Alibaba’s AI capabilities with Spring. It’s packed with potential, but like any ambitious codebase, it’s not immune to architectural slip-ups.
My goal was simple: unleash Claude Code on it, armed with a prompt inspired by Clean Code and Clean Architecture, and see what Uncle Bob would have to say.
Here’s the prompt I fed into Claude Code 🧑💻
Analyze the code in the current directory as if you were Robert C. Martin. Apply the principles from Clean Code and Clean Architecture to identify architectural and code violations. For each issue found, create a structured Jira ticket with:
Title: Clear description of the issue
Description: What’s wrong and why it violates best practices
Story Points: 1-8 based on complexity
Priority: High/Medium/Low
Severity: Critical/Major/Minor/Trivial
Recommendation: Specific steps to fix the issue
Focus on the most critical issues first.
The output was actionable insights formatted as tickets eight of them, to be exact. Let’s break it down and explore how Claude Code turned into my personal code quality enforcer.
What is Claude Code, Anyway? 🤔
Claude Code isn’t a standalone tool, it’s what happens when you leverage Anthropic’s Claude model (or a similar AI) to analyze and critique codebases. Think of it as a super-smart pair programmer who’s read every page of Clean Code and isn’t afraid to call you out. In my case, I ran this prompt through a setup that could process the Spring AI Alibaba repo, likely using a combination of Claude’s natural language processing and some custom scripting to parse the Java files and structure the output.
The beauty of Claude Code lies in its ability to not just spot issues but explain why they’re problems and how to fix them all in a way that feels human and practical. It’s a technical architect who is working on your command.
How to setup Claude Code ? 🧑💻
It is pretty straightforward,
Checkout the code base https://github.com/anthropics/claude-code
Install the packages npm install -g @anthropic-ai/claude-code
No fancy setup required—just a codebase and a goal.
How Claude Code Works ? 🔬
So, what’s under the hood? Claude Code is an AI-powered analysis tool that:
Parses Code: It reads your Java files, understanding structure and logic.
Applies Rules: Using principles like SOLID and Clean Code, it flags violations based on your prompt.
Generates Output: It structures findings into actionable tickets, tailored to your needs (e.g., Jira format).
Here is the Claude Code in action👇 (recommended speed 2x)
In my case, I fed it a custom prompt to emulate Uncle Bob’s perspective. The AI then analyzed the Alibaba Spring AI library, cross-referencing its code against best practices. The result was a detailed, prioritized list of issues something a manual review might take hours (or days) to achieve.
The Findings: Uncle Bob’s Sprint
Claude Code didn’t hold back. It churned out eight tickets, each targeting a specific violation of clean coding practices. Here’s a rundown of the highlights and trust me, these are gold for any dev looking to level up their craft.
Key Findings: What Claude Code Uncovered
Claude Code didn’t hold back—it flagged eight issues, each tied to Clean Code or SOLID principles. Here’s a rundown of what it found, why it matters, and how to fix it:
1. Refactor DashScopeChatModel to Follow Single Responsibility Principle
What’s Wrong: The DashScopeChatModel class is a beast—over 500 lines long, juggling request creation, response mapping, media conversion, and tool/function calls. This violates the Single Responsibility Principle (SRP).
Severity: Critical | Priority: High | Story Points: 8
Why It Matters: A class with too many jobs is a maintenance nightmare—hard to test, debug, or extend.
Fix: Split it into smaller classes, each with one clear responsibility (e.g., stream handling, tool calling). Use a facade pattern to keep the public API intact.
2. Implement Consistent Error Handling Strategy
What’s Wrong: Error handling is a mixed bag—some methods swallow exceptions, others lack a standard pattern. No consistent exception hierarchy exists.
Severity: Major | Priority: High | Story Points: 5
Why It Matters: Inconsistent error handling leads to unpredictable bugs and hours lost in debugging.
Fix: Create a unified exception hierarchy, wrap exceptions to preserve stack traces, and standardize error codes across the codebase.
3. Reduce Excessive Fields in DashScopeChatOptions
What’s Wrong: The DashScopeChatOptions class has over 20 fields, hinting at too many responsibilities and violating Interface Segregation.
Severity: Major | Priority: Medium | Story Points: 5
Why It Matters: Bloated classes mean complex initialization and tighter coupling—both Clean Code no-nos.
Fix: Use the Composite pattern to group related options into smaller classes (e.g., auth options, model params).
4. Implement Comprehensive Logging Strategy
What’s Wrong: Logging is spotty—limited SLF4J use, inconsistent levels, and no correlation IDs for tracing.
Severity: Major | Priority: Medium | Story Points: 3
Why It Matters: Weak logging hampers observability, making it tough to troubleshoot issues in production.
Fix: Standardize logging with SLF4J, add correlation IDs, and define exception-logging patterns.
5. Extract Hardcoded Configuration
What’s Wrong: Configurations are hardcoded, breaking Clean Architecture’s configuration principle.
Severity: Minor | Priority: Medium | Story Points: 2
Why It Matters: Hardcoding limits flexibility across environments (dev, prod, etc.).
Fix: Move configs to properties files or classes and use a configuration provider pattern.
6. Fix Incomplete Test Coverage in Advisor Components
What’s Wrong: Files like DocumentRetrievalAdvisorTests.java are empty—no tests for critical components.
Severity: Major | Priority: Medium | Story Points: 3
Why It Matters: Missing tests invite regressions and erode confidence in the code.
Fix: Write comprehensive tests, including edge cases, using a Test-Driven Development (TDD) approach.
7. Standardize Naming Conventions
What’s Wrong: Naming is all over the place—mixed abbreviations, inconsistent styles.
Severity: Minor | Priority: Low | Story Points: 2
Why It Matters: Inconsistent naming slows down code comprehension, clashing with Clean Code’s readability focus.
Fix: Define naming rules and refactor accordingly.
8. Break Down Oversized DashScopeApi Class
What’s Wrong: The DashScopeApi class is a 1400-line monster, violating SRP with too many duties.
Severity: Critical | Priority: High | Story Points: 8
Why It Matters: Oversized classes are maintenance sinks and bug magnets.
Fix: Split it into domain-specific API clients and extract shared HTTP logic using the Template Method pattern.
Why This Matters to You
Fixing these issues isn’t just about cleaning up code—it’s about building software that lasts. Adhering to SOLID principles and Clean Code practices makes your codebase:
Easier to Maintain: Smaller, focused classes are simpler to update.
More Testable: Clear responsibilities mean straightforward unit tests.
Scalable: SOLID designs adapt to change without breaking everything.
Claude Code shines here by automating the grunt work. It’s like having an expert reviewer who spots issues you might miss in the daily grind. They’re a roadmap to better code, complete with priorities and effort estimates.
Technical Takeaways for Devs
So, how does Claude Code pull this off? While I don’t have the exact internals ( Anthropic’s magic sauce is, well, magical), here’s what’s likely happening under the hood:
Code Parsing: The tool scans the Java files, building an abstract syntax tree (AST) or similar representation to understand structure and dependencies.
Pattern Matching: Based on the prompt, it applies heuristics based on Clean Code and Clean Architecture think line counts, method complexity, or coupling metrics to flag violations.
Natural Language Generation: Using Claude’s language skills, it crafts human-readable tickets, weaving in Uncle Bob’s principles like SRP or dependency inversion.
Scoring & Prioritization: It assigns story points and severity based on impact and effort, likely using a pre-trained model fine-tuned on software engineering data.
This approach is replicable with some elbow grease. Pair a code analysis tool (like SonarQube) with an AI model (Claude, GPT, or even Grok!), and you’ve got a DIY Claude Code setup. Feed it a prompt like mine, tweak the output format, and watch the tickets roll in.
Why This Matters
Using Claude Code on Spring AI Alibaba wasn’t just a fun experiment—it’s a glimpse into the future of software development. AI isn’t here to replace us; it’s here to amplify us. Imagine running this on your own project: instant feedback, structured tasks, and a push toward SOLID, maintainable code. It’s like having Uncle Bob on speed dial, minus the coffee-stained book.
For the Spring AI Alibaba maintainers, these tickets could kickstart a refactor sprint. And for the broader dev community, it’s a reminder: clean code isn’t optional—it’s the foundation of scalable, reliable software.
Wrapping Up
My adventure with Claude Code and Spring AI Alibaba was equal parts humbling and inspiring. After all my wild experiments with Claude Code, my credits went up in smoke, and Anthropic just disabled my API access 🤑 😭
If you’re a developer itching to polish your codebase or just curious about AI-driven reviews give this approach a shot. Clone a trending repo, fire up Claude Code, and let Uncle Bob guide you to cleaner, better software. Also make sure you have enough credits to burn :)
I believe software engineering is an art and any tooling can only make an artist's life more productive.