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World Congress 2025 • Aug 20, 2025 • Session details

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

Markus Harrer

Stop dumping massive legacy codebases into standard LLM prompts. Instead, apply AI-driven software archeology to excavate git histories, untangle technical debt, and reveal hidden architectural intent.

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#1 about 3 min

Challenges of applying artificial intelligence to legacy software systems

Modern text generation models often struggle to process huge, undocumented legacy codebases compared to newly structured projects.

#2 about 3 min

Understanding legacy code through software archeology analysis techniques

Applying investigative techniques from archeology helps developers answer fundamental questions about the structure and origins of abandoned code.

#3 about 3 min

Visualizing system layouts using the Wheeler-Kenyon excavation method

Mapping directories and file metadata onto grid-based tree maps visually highlights the oldest and most actively developed architectural components.

#4 about 2 min

Generating custom codebase analytics scripts with artificial intelligence

Artificial intelligence assistants can automatically generate Python data analysis scripts to construct customized visualizations of repository metadata.

#5 about 4 min

Applying typology to categorize software architecture patterns and concepts

Grouping scattered source files into technical and business classifications establishes a higher-level abstraction for comprehending system frameworks.

#6 about 7 min

Using artificial intelligence models to extract business concept maps

Prompting code models to extract distinct technical concepts maps existing source files to documented domain behaviors to expose framework code.

#7 about 6 min

Calculating conceptual integrity scores for legacy source files

Scoring how consistently individual files implement abstract architectural concepts highlights mixed features and untrustworthy components.

#8 about 5 min

Reconstructing component history with the chaine operatoire technique

Supplying raw commit logs to text generation models produces detailed historical timelines explaining why specific system components evolved.

Matching moments

6:27 min

Using AI copilots to explain and debug legacy codebases

Chris Heilmann +2 · LIVE

1:57 min

Analyzing legacy systems using software data

Markus Harrer Markus Harrer · WWC 2021

3:03 min

Evaluating AI agents for iterative legacy code modernization

Michael Friedrich Michael Friedrich · WWC Europe 2026

5:39 min

Managing legacy infrastructure and distributed software systems

Daniel Geisel Daniel Geisel +1 · WWC Europe 2026

1:05 min

Modernizing legacy code repositories for robust artificial intelligence

April Yoho April Yoho · WWC Europe 2026

2:20 min

Reverse engineering legacy codebases with large language models

Mateusz Gren Mateusz Gren +1 · WWC Europe 2026

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