Markus Harrer

Getting to Know Your Legacy (System) with AI-Driven Software Archeology

What if AI could act as your personal software archeologist? Learn three techniques to excavate, classify, and understand the hidden history of any legacy system.

Getting to Know Your Legacy (System) with AI-Driven Software Archeology
#1about 5 minutes

Applying archaeological techniques to legacy software systems

Legacy systems present challenges like poor documentation and missing context, which can be addressed by applying archaeological methods to understand their history and structure.

#2about 4 minutes

Using excavation to map your legacy codebase

The Wheeler-Kenyon method can be adapted to software by creating a grid-like treemap of a codebase to visualize file age and development hotspots.

#3about 10 minutes

Identifying code patterns with AI-driven typology

Typology involves classifying scattered source code files into technical and business concepts, a repetitive task that large language models can automate.

#4about 5 minutes

Scoring the conceptual integrity of software components

An LLM can score how well a piece of code implements its intended concept, helping to identify trustworthy and mixed-up parts of the system.

#5about 4 minutes

Reconstructing component history with chaîne opératoire

The chaîne opératoire technique uses an LLM to analyze commit history and generate a timeline of a component's evolution, revealing its purpose and key contributors.

#6about 1 minute

How to effectively leverage AI for legacy code

Successfully using AI on legacy systems requires breaking down the problem and providing specific context rather than feeding the entire codebase to a model.

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