5 papers
Code Health in LLM-Based Test Generation: Effectiveness and Token Efficiency
Freya Wirdemann, Markus Borg, Nadim Hagatulah +1
Coding agents powered by Large Language Models (LLMs) are now prominent in software engineering. Previous work has shown that AI tools perform better on high-quality source code th…
Technical Debt Friction for Maintenance Prioritization: An Industrial Multi-Case Study
Simeon Tverdal, Phu Nguyen, Arda Goknil +3
Software-intensive organizations need effective ways to identify where maintenance and refactoring efforts will yield the greatest practical benefit. Although software analytics su…
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…
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…
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…