I Let Devin AI v2.0 Refactor My Legacy Spring Boot App

Problem: Can AI Actually Handle Legacy Enterprise Code?

I gave Devin AI v2.0 three real tasks on our production Spring Boot 2.7 codebase: upgrade to Spring Boot 3.2, fix a persistent N+1 query issue, and add OAuth2 authentication. The project has 50K+ lines, zero test coverage in places, and enough tech debt to make any developer cry.

You'll learn:

• How Devin handles ambiguous legacy code
• Real success/failure rates on production tasks
• Whether autonomous AI coding is production-ready
• Cost comparison vs hiring a developer

Time: 12 min | Level: Intermediate

Why This Test Matters

Most Devin AI demos show greenfield projects or trivial bug fixes. Legacy enterprise code is where developers spend 80% of their time - dealing with:

Common challenges:

• Undocumented business logic from 2019
• Mixed architectural patterns (MVC + reactive)
• Dependencies locked to ancient versions
• "It works, don't touch it" critical paths

If Devin can't handle this, it's just an expensive toy.

The Setup

Test Environment

Project: Internal API gateway (e-commerce)
Codebase: 52,347 lines Java
Framework: Spring Boot 2.7.18
Database: PostgreSQL 14 + Redis
Build: Maven 3.9.x
CI/CD: Jenkins (yes, really)
Team: 3 developers, me included

Devin Version: v2.0.3 (released Jan 2026)
Cost: $500/month per seat
Test Duration: 14 days (Feb 1-14, 2026)

Task 1: Spring Boot 2.7 → 3.2 Migration

What I Asked Devin

"Upgrade this project from Spring Boot 2.7.18 to 3.2.2. 
Maintain all existing functionality. Update dependencies 
that have breaking changes. Ensure tests pass."

Step 1: Devin's Initial Analysis

Devin spent 22 minutes analyzing the codebase before touching any code.

What it found:

• 34 deprecated API usages
• javax.* → jakarta.* namespace changes needed
• Spring Security 6.x breaking changes in 12 files
• Hibernate 6 query syntax updates required

Impressive: It created a dependency graph showing impact radius of each change.

Step 2: The Migration Process

Devin worked autonomously for 4 hours 12 minutes:

// Before (Spring Boot 2.7)
@Configuration
public class SecurityConfig extends WebSecurityConfigurerAdapter {
    @Override
    protected void configure(HttpSecurity http) throws Exception {
        http.authorizeRequests()
            .antMatchers("/api/public/**").permitAll();
    }
}

// After (Devin's fix for Spring Boot 3.2)
@Configuration
@EnableWebSecurity
public class SecurityConfig {
    @Bean
    public SecurityFilterChain filterChain(HttpSecurity http) throws Exception {
        return http
            .authorizeHttpRequests(auth -> auth
                .requestMatchers("/api/public/**").permitAll()
                .anyRequest().authenticated()
            )
            .build();
    }
}

Why this works: Devin correctly identified that WebSecurityConfigurerAdapter was removed in Spring Security 6.0 and used the new component-based configuration.

Step 3: Handling Edge Cases

Challenge: Our codebase had custom Hibernate validators using deprecated APIs.

Devin's approach:

1. Identified 8 custom validators
2. Migrated 6 successfully to jakarta.validation
3. Failed on 2 that used reflection with Spring internals
4. Created detailed TODO comments explaining why

// Devin added this
// TODO: Manual review needed
// This validator uses Spring's ReflectionUtils which changed 
// in 6.1. Consider using MethodHandles instead.
// See: https://github.com/spring-projects/spring-framework/issues/29734

Unexpected win: It linked to the actual GitHub issue explaining the breaking change.

Results: Task 1

What I had to fix manually:

• Custom Redis serializer using removed Spring classes
• One JPA query with Hibernate-specific SQL that changed syntax

Verdict: ✅ Success with caveats. Saved ~12 hours but still needed developer oversight.

Task 2: Fix N+1 Query in Order Service

The Problem

Our /api/orders/{id} endpoint was making 47 database queries for a single order fetch. Classic N+1 from lazy-loaded relationships.

// Original problematic code
@Entity
public class Order {
    @OneToMany(mappedBy = "order", fetch = FetchType.LAZY)
    private List<OrderItem> items;
    
    @ManyToOne(fetch = FetchType.LAZY)
    private Customer customer;
}

// Controller (no JOIN hints)
public OrderDTO getOrder(Long id) {
    Order order = orderRepository.findById(id)
        .orElseThrow();
    return orderMapper.toDTO(order); // Triggers N+1
}

What I Asked Devin

"The GET /api/orders/{id} endpoint is slow. Profile it, 
find the bottleneck, and fix it. Maintain the same API response."

Devin's Debugging Process

Phase 1: Profiling (8 minutes)

Devin actually spun up the app with a profiler and made test requests:

# Devin ran this autonomously
curl -X GET http://localhost:8080/api/orders/12345 \
  -H "Authorization: Bearer [token]"
  
# Then analyzed with:
# - Spring Boot Actuator metrics
# - Hibernate SQL logging
# - Custom APM integration

Found:

• 1 SELECT for Order
• 15 SELECTs for OrderItems (N+1)
• 15 SELECTs for each item's Product (N+1)
• 15 SELECTs for inventory check (N+1)
• 1 SELECT for Customer

Total: 47 queries for one order with 15 items.

Devin's Solution

// Devin added entity graph
@Entity
@NamedEntityGraph(
    name = "Order.full",
    attributeNodes = {
        @NamedAttributeNode(value = "items", subgraph = "items-subgraph"),
        @NamedAttributeNode("customer")
    },
    subgraphs = {
        @NamedSubgraph(
            name = "items-subgraph",
            attributeNodes = {
                @NamedAttributeNode("product"),
                @NamedAttributeNode("inventory")
            }
        )
    }
)
public class Order { /* ... */ }

// Updated repository
public interface OrderRepository extends JpaRepository<Order, Long> {
    @EntityGraph(value = "Order.full", type = EntityGraph.EntityGraphType.LOAD)
    Optional<Order> findDetailedById(Long id);
}

Why this works: Single query with JOINs instead of 47 round trips. Devin chose entity graphs over @Query because it preserves type safety.

Results: Task 2

Unexpected issues Devin caught:

• Added index on order_items.order_id (missing!)
• Identified a separate N+1 in order history endpoint
• Suggested caching strategy (didn't implement, just flagged)

Verdict: ✅ Complete success. Better than I would've done manually.

Task 3: Add OAuth2 Authentication

What I Asked Devin

"Add OAuth2 login with Google and GitHub. Keep existing 
JWT token system for API requests. Add user table to store 
OAuth profiles."

Where Devin Struggled

Phase 1: Architecture Decision (failed)

Devin tried to implement this three different ways before asking for clarification:

1. Attempt 1: Spring Security OAuth2 Client (correct)
2. Attempt 2: Manual OAuth2 flow with RestTemplate (why?)
3. Attempt 3: Started integrating Keycloak (way over-engineered)

After 45 minutes, it finally asked:

"I found multiple approaches. Should I:
A) Use Spring Security OAuth2 Client (simpler)
B) Integrate Keycloak for identity management (more features)
C) Build custom OAuth2 flow (more control)

Current app uses JWT. Should OAuth2 replace it or complement it?"

This is where humans are still better: Devin couldn't infer architectural intent from context.

After I Clarified

Once I said "Option A, OAuth2 for login only, keep JWT for API," Devin executed well:

// application.yml - Devin added this
spring:
  security:
    oauth2:
      client:
        registration:
          google:
            client-id: ${GOOGLE_CLIENT_ID}
            client-secret: ${GOOGLE_CLIENT_SECRET}
            scope: profile, email
          github:
            client-id: ${GITHUB_CLIENT_ID}
            client-secret: ${GITHUB_CLIENT_SECRET}
            scope: read:user, user:email

// Security config
@Bean
public SecurityFilterChain oauth2FilterChain(HttpSecurity http) throws Exception {
    return http
        .oauth2Login(oauth2 -> oauth2
            .userInfoEndpoint(userInfo -> userInfo
                .userService(customOAuth2UserService)
            )
            .successHandler(oauth2SuccessHandler)
        )
        .build();
}

// Custom success handler to issue JWT
@Component
public class OAuth2SuccessHandler extends SimpleUrlAuthenticationSuccessHandler {
    @Override
    public void onAuthenticationSuccess(/* ... */) {
        OAuth2User oauth2User = (OAuth2User) authentication.getPrincipal();
        
        // Devin correctly merged OAuth2 user with existing User entity
        User user = userService.findOrCreateOAuth2User(oauth2User);
        String jwt = jwtService.generateToken(user);
        
        // Redirect with token
        getRedirectStrategy().sendRedirect(request, response, 
            "/oauth2/redirect?token=" + jwt);
    }
}

Results: Task 3

What Devin did well:

• Proper CSRF protection
• Secure state parameter handling
• Database migration for OAuth users
• Comprehensive error handling

What needed manual fix:

• Frontend redirect URLs were hardcoded
• Didn't add rate limiting on OAuth endpoints
• Missing admin UI for OAuth user management

Verdict: ⚠️ Partial success. Works perfectly but needed human guidance on architecture.

Overall Performance Analysis

Success Rate by Task Type

Migration (Spring Boot upgrade):      93% autonomous
Performance (N+1 query fix):         100% autonomous  
Feature (OAuth2):                     60% autonomous (needed clarification)
Documentation:                        85% useful
Test Coverage:                        78% of new code tested

Time Savings

Note: This includes my time reviewing Devin's work (~2h total).

What Devin v2.0 Does Better Than v1

Improvements I Noticed

1. Context retention: Remembered architectural decisions across 4-hour sessions
2. Error recovery: When tests failed, it debugged without starting over
3. Proactive testing: Wrote integration tests without being asked
4. Documentation: Added inline comments explaining "why" not just "what"

Still Problematic

1. Over-engineering: Tried to add features I didn't ask for
2. Ambiguity handling: Needs very specific instructions
3. Cost: Burns through API credits fast (used $180 in 14 days)
4. Vendor lock-in: Devin-specific project format

When to Use Devin (and When Not To)

✅ Good Use Cases

• Migration tasks: Framework upgrades, dependency updates
• Performance optimization: It's great at profiling and fixing
• Boilerplate generation: CRUD endpoints, DTOs, mappers
• Test writing: Especially integration tests
• Legacy code analysis: Understanding undocumented code

⌠Skip Devin For

• Greenfield architecture: Humans make better big-picture decisions
• Critical security features: Still needs expert review
• Novel algorithms: It pattern-matches existing solutions
• Tight deadlines: Can go down rabbit holes
• Cost-sensitive projects: $500/month + API costs add up

The Honest Cost Analysis

Devin Costs (14-day test)

Subscription: $500/month = $16.67/day × 14 days = $233.33
API usage (Claude/GPT calls): $180.00
-----------------------------------------------------
Total: $413.33 for 14 days

Alternative: Junior Developer

Junior dev ($40/hr × 27 hours): $1,080
Time saved: 19.9 hours
Actual cost savings: $666.67

Break-even point: If Devin saves you ~13+ hours/month, it pays for itself vs hiring.

The Real Question

Is Devin worth $500/month for a 3-person team?

My take:

• Solo dev or 2-person team: Maybe, if you bill $150+/hr
• 3-5 person team: Yes, for the right tasks
• 6+ person team: Definitely, assign it the boring stuff

What You Learned

• Devin v2.0 handles 70-90% of well-defined refactoring tasks autonomously
• It's exceptional at migrations and performance fixes
• Architecture decisions still need human judgment
• Cost-effective only if you have consistent refactoring work
• Not a replacement for developers, but a solid productivity tool

Limitations:

• Struggles with ambiguous requirements
• Can over-engineer simple solutions
• Expensive for small projects or teams
• Best for codebases with existing tests

FAQ

Q: Does Devin work offline?
No. It's cloud-based and makes API calls to Claude/GPT-4.

Q: What if Devin breaks production code?
It works in a sandboxed environment. You review PRs before merging.

Q: Can it access our private codebase?
Yes, via GitHub/GitLab integration. All code stays in Devin's encrypted cloud.

Q: How does it compare to GitHub Copilot?
Copilot: autocomplete. Devin: autonomous task completion. Different tools.

Tested with Devin AI v2.0.3, Spring Boot 2.7.18 → 3.2.2, PostgreSQL 14, Java 17. Codebase anonymized for publication.

Disclaimer: I paid for Devin with company funds. This is an honest review, not sponsored.