Arqueología de infraestructura: RE con un LLM de copiloto

In the article "Arqueología de infraestructura: RE con un LLM de copiloto," Camilo discusses the practice of connecting large language models (LLMs) to infrastructure without proper documentation. This is not a new trend, but rather a discipline that has been around for 35 years. In 1990, researchers Elliot Chikofsky and James Cross defined the six stages of reverse engineering, which includes examining a system to understand its architecture and generate a description at a higher level of abstraction.

The author of a Terraform infrastructure using Azure, a Senior Tech Architect, describes a scenario where the infrastructure is well-architected but lacks a complete understanding of the system. To address this, the architect connects an LLM to the repository via MCP and starts asking questions. The LLM helps uncover hidden dependencies, technical debt, and decisions made years ago. However, the architect must validate, refute or hold these suggestions while the LLM provides hypotheses.

The article highlights the distinction between forward engineering (building a system) and reverse engineering (understanding an existing system). Reverse engineering involves examining a system without changing it, whereas forward engineering involves creating a new system based on a description. The author emphasizes the importance of bringing back the context and domain knowledge that was initially lost when the system was built.

The article also mentions that while the definition of reverse engineering has not changed since 1990, the process has evolved with the introduction of LLMs. The LLM assists the architect in generating hypotheses and deciding which ones to validate or disregard. This distinction between the cognitive work of the architect and the assistance provided by the LLM is subtle but important, as it explains why the task still takes weeks rather than minutes despite the presence of an LLM.

Interestingly, the industry has already adopted the term "reverse engineering" for this practice, while the academic community has not yet caught up. The article concludes by mentioning that there is a gap in formal research on using LLMs for understanding existing infrastructure, as most published papers focus on generating new infrastructure from natural language descriptions.