What $174 and 28K Lines of C-to-Rust Transpilation Actually Taught Us About AI Code Migration

Here's the brutal reality: Migrating legacy C codebases to memory-safe Rust isn't just a nice-to-have anymore—it's becoming a business necessity. When over 50% of security exploits stem from memory safety bugs that Rust's borrow checker prevents by design, the question isn't whether to migrate, but how.

One developer just provided us with hard data by spending $174 of their own money to transpile 12 open-source C projects (28,000 lines total) to Rust using AI agents. The results reveal both the promise and pitfalls of automated code migration at a scale that matters.

The Real Economics of AI-Assisted Migration

At roughly $0.006 per line of code, the raw economics look compelling. Compare that to hiring senior Rust developers at $150-200/hour for manual migration, and you're looking at potentially 100x cost savings for the initial conversion.

But here's where it gets interesting: recent research shows this cost-first approach misses critical nuances. The SafeTrans framework, tested on 2,653 C programs, achieved an 80% success rate using GPT-4o with iterative error repair—but only after multiple expensive passes:

1// What AI often produces initially (unsafe but functional)
2unsafe {
3    let ptr = malloc(size) as *mut u8;
4    *ptr = value;
5    return ptr;
6}
7
8// What you actually want (idiomatic Rust)
9fn allocate_and_set(size: usize, value: u8) -> Result<Vec<u8>, AllocationError> {
10    let mut buffer = vec![0u8; size];
11    buffer = value;
12    Ok(buffer)
13}

The gap between "it compiles" and "it's actually safe Rust" represents the hidden cost that raw per-line pricing doesn't capture.

Where AI Excels (and Where It Fails Spectacularly)

The data reveals a clear pattern: AI handles mechanical translation well but struggles with architectural decisions. Tools like C2Rust achieve 99.67% functionality preservation—essentially creating executable unsafe Rust that mirrors C behavior exactly.

For complex systems programming patterns, the failure modes are predictable:

1// C code with implicit ownership semantics
2void process_data(void* data, size_t len, void (*cleanup)(void*)) {
3    // ... processing ...
4    if (should_cleanup) cleanup(data);
5}

AI often produces Rust that compiles but misses the ownership semantics entirely, creating potential use-after-free bugs that Rust's type system should prevent.

The Hybrid Approach That Actually Works

The most successful teams aren't using pure AI or pure manual effort—they're combining tools strategically:

1. Start with C2Rust for mechanical translation (high fidelity, produces unsafe Rust)

2. Apply LLM-based refinement using frameworks like SafeTrans for iterative improvement

3. Human review for critical paths, especially cryptographic code and complex pointer manipulation

4. Rigorous testing with the original C test suites to verify behavioral equivalence

This hybrid approach addresses what I call the "island problem"—patches of unsafe Rust that undermine the memory safety benefits you're migrating to achieve.

What the DARPA Investment Signals

DARPA's TRACTOR program funding for automated C-to-Rust tools isn't just academic interest—it's recognition that manual migration doesn't scale to the millions of lines of critical C infrastructure powering everything from embedded systems to OS kernels.

The program's goal of "skilled-developer-quality output" sets a high bar that current tools haven't reached. But the $174 experiment shows we're getting closer to practical viability for certain classes of code.

The Verification Problem

Here's what the cost-focused discussion often misses: verification that the Rust code actually eliminates the original vulnerabilities. Recent work shows that successful transpilation can indeed prevent buffer overflows and use-after-free bugs that were present in the original C, but only if the conversion properly handles implicit C behaviors around pointer ownership.

For cryptographic code—which featured in several of the 12 test projects—this verification becomes critical. A subtle timing attack vulnerability in C needs to be explicitly addressed in the Rust version, not just mechanically translated.

Practical Takeaways for Your Next Migration

If you're considering AI-assisted C-to-Rust migration:

Budget for iteration: That $174 likely represents multiple passes and refinements. Plan for 3-5x the initial estimate.

Start with isolated modules: Target self-contained libraries with good test coverage before attempting monolithic codebases.

Invest in verification tooling: The cost of missed vulnerabilities far exceeds the savings from faster conversion.

Consider mathematically proven alternatives: Tools like KAIST's converter sacrifice speed for correctness guarantees—sometimes that trade-off makes sense.

Why This Matters Right Now

The timing of this experiment isn't coincidental. Major projects like the Linux kernel are already integrating Rust components, and browser vendors are migrating performance-critical C++ to Rust for security reasons.

The window for gradual migration is closing. Organizations that develop migration expertise now—understanding both the capabilities and limitations of AI-assisted tools—will have a significant advantage over those that wait for "perfect" solutions.

The $174 experiment provides a data point, but more importantly, it demonstrates that systematic evaluation of AI code migration is both feasible and necessary. The question isn't whether AI will transform systems programming—it's whether your team will be ready when it does.