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20242026
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cs.SE2026

Code for Machines, Not Just Humans: Quantifying AI-Friendliness with Code Health Metrics

Markus Borg, Nadim Hagatulah, Adam Tornhill +1

We are entering a hybrid era in which human developers and AI coding agents work in the same codebases. While industry practice has long optimized code for human comprehension, it…

cs.SE2025

ACE: Automated Technical Debt Remediation with Validated Large Language Model Refactorings

Adam Tornhill, Markus Borg, Nadim Hagatulah +1

The remarkable advances in AI and Large Language Models (LLMs) have enabled machines to write code, accelerating the growth of software systems. However, the bottleneck in software…

cs.SE2025

Echoes of AI: Investigating the Downstream Effects of AI Assistants on Software Maintainability

Markus Borg, Dave Hewett, Nadim Hagatulah +5

[Context] AI assistants, like GitHub Copilot and Cursor, are transforming software engineering. While several studies highlight productivity improvements, their impact on maintaina…

cs.SE2024

Industrial Code Quality Benchmarks: Toward Gamification of Software Maintainability

Markus Borg, Amogha Udayakumar, Adam Tornhill

Software maintainability is essential for long-term success in the software industry. Despite widespread evidence of the high costs associated with poor maintainability, market pre…

cs.SE2024

Does Co-Development with AI Assistants Lead to More Maintainable Code? A Registered Report

Markus Borg, Dave Hewett, Donald Graham +4

[Background/Context] AI assistants like GitHub Copilot are transforming software engineering; several studies have highlighted productivity improvements. However, their impact on c…

cs.SE20241 cited

Ghost Echoes Revealed: Benchmarking Maintainability Metrics and Machine Learning Predictions Against Human Assessments

Markus Borg, Marwa Ezzouhri, Adam Tornhill

As generative AI is expected to increase global code volumes, the importance of maintainability from a human perspective will become even greater. Various methods have been develop…