Markus Borg

Dr. Markus Borg is a Senior Researcher and Adjunct Lecturer at Lund University, Sweden, specializing in the intersection of software engineering and applied artificial intelligence. He is a Principal Researcher at CodeScene and contributes to editorial boards for Empirical Software Engineering and IEEE Software. His work bridges academic research with industrial practice through collaborations with Ericsson and contributions to open-source tools like SMIRK.

Markus's research focuses on empirical software engineering, technical debt, safety engineering, and requirements engineering for AI-integrated systems. He explores two key directions: AI4SE (applying machine learning to software engineering challenges like defect management and technical debt remediation) and SE4AI (ensuring quality assurance for ML components in safety-critical domains such as automotive systems). His work aligns with regulatory frameworks like the EU AI Act and industry standards for automotive safety.

• 2025 Contributions:
  • Gamify Track: Two papers on gamification in software maintainability
  • ICSE Journal-First: Longitudinal study on automated bug assignment at Ericsson
  • IDE Track: Trust calibration in AI-assisted refactoring
  • TechDebt Technical Papers: ACE tool for LLM-based technical debt remediation
• 2024 Contributions:
  • PROFES: AI Act compliance in requirements engineering
  • MO2RE: Code quality specifications
  • TechDebt: Maintainable code ROI analysis
  • Programming with AI: CodeScene platform demonstration
• 2023 Contributions:
  • CAIN: ML testing in automotive perception systems
  • EASE: Code ownership and defect resolution
  • Mutation Track: Safety-critical mutation testing validation

His recent publications emphasize automated defect management, LLM-driven refactoring, and safety validation for ML components, particularly in automotive contexts. Tools like SMIRK and ACE demonstrate practical applications of his research. Markus actively participates in program committees for ICSE, TechDebt, and ICSME, with a focus on bridging academic research with industrial AI/SE challenges.