Markus Harrer
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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