AI Can Write Code. But Can It Maintain It ?

Introduction

Over the past few years, AI tools capable of generating code have made impressive progress. In seconds, they can write functions, fix bugs, and help build simple applications. This rapid evolution has led many people to ask an important question: will AI replace developers?

A recent study conducted by researchers at Alibaba suggests the answer is more nuanced. While AI models are increasingly good at generating code, maintaining and evolving that code over time remains a major challenge. And in real-world software development, writing code is only the beginning.

Writing Code Is Only the Beginning

Most benchmarks used to evaluate AI coding systems focus on whether the code works at a specific moment. Models are often asked to solve programming problems, fix bugs, or generate functions that pass a set of tests. If the tests pass, the model succeeds.

But this approach overlooks an important reality. Developers rarely work on completely new code. Most of their time is spent modifying and improving existing systems, adding features, and making sure that new changes do not break what already works. Software projects evolve continuously, often over months or years something traditional benchmarks rarely measure.

Testing AI on Real Code Evolution

To better reflect real development work, researchers introduced a benchmark called SWE-CI, designed to evaluate how AI agents manage the evolution of a codebase over time. Instead of isolated tasks, the benchmark simulates a real project evolving through multiple consecutive code changes, where the AI must update the code while preserving existing functionality. In other words, the AI must do what developers do every day: improve a codebase without breaking it.

What the Results Show

The results highlight an important limitation of current AI coding systems. In about 75% of cases, the models eventually introduced regressions, meaning that their modifications broke code that previously worked. This often happens because many models are optimized to pass tests immediately rather than maintain long-term stability.

Among the tested systems, Claude Opus 4 achieved some of the strongest results, but even the best-performing models still struggle to maintain complex projects as reliably as experienced developers.

AI as a Copilot

These results do not mean that AI has no role in software development. In fact, AI tools are becoming powerful assistants for developers. They can help generate code, explain complex logic, and automate repetitive tasks. Rather than replacing developers, AI currently acts more like a copilot, helping engineers work faster while they design and maintain complex systems.

Building Developers Who Think Beyond Code

For people learning to code today, this evolution highlights an important reality: programming is not only about writing code. It is about understanding systems, designing reliable architectures, and maintaining software over time.

At Holberton School, students learn to work on real projects, understand existing codebases, and build systems that can evolve and remain reliable in the long term. Because in the tech industry, the real challenge is not just writing code quickly. It is building software that lasts.

Sources : 

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• Chen, J. et al. (2026) — SWE-CI: Evaluating Agent Capabilities in Maintaining Codebases via Continuous Integration
  
  
• SWE-CI Benchmark
  
  
• SWE-bench Benchmark
  
  
• HumanEval Benchmark