LATEST NEWS:

Apr 27 – Giving talks at TechDebt25, ICSE25, Gamify25, and IDE25.

Apr 9 – Organizing the Industry Track at REFSQ25!

Jan 15 – Attended the WASP Winter Conference 2025.

About Dr. Borg

Dr. Markus Borg is a leading figure in the confluence of software engineering and applied artificial intelligence. With dual roles as a principal researcher at CodeScene and an adjunct associate professor at Lund University, his work primarily focuses on enhancing the engineering of software and data-intensive systems through innovative research. He is especially passionate about the maintainability of software, striving to improve code health and sustainability over its lifecycle. Dr. Borg serves on the editorial board of Empirical Software Engineering and is a department editor for IEEE Software. He is a faculty member of WASP.

Research Focus

While my research interests are broad, most of my work intersects software engineering and machine learning. I find it intriguing to explore both sides of the coin: Software Engineering Intelligence (AI4SE) and AI Engineering (SE4AI).

In AI4SE, I seek to tap into the collected wisdom of historical project data to facilitate machine learning for actionable decision support. My most impactful contributions have been related to defect management, for example, bug assignment and change impact analysis. Core ideas are currently operationalized in internal tools at Ericsson.

In SE4AI,  I investigate quality assurance of systems that incorporate machine learning components. I am particularly interested in development mandated by automotive safety standards and the new EU AI Act. Our research studies involve requirements engineering, MLOps pipelines, software testing in automotive simulators, and our open-source demonstrator SMIRK.

Current Supervision

• Matthias Wagner, PhD Student, Lund University, 2023- (Main supervisor)
• Mirza Selimovic, PhD Student, International University of Sarajevo, 2024- (Main supervisor)
• Nadim Hagatulah, PhD Student, Lund University, 2025- (Main supervisor)
• Dimitris Paraschakis, Postdoc, Theca Systems and Lund University, 2023- (Co-supervisor)
• Rushali Gupta, PhD Student, Lund University, 2024- (Co-supervisor)

Previous Supervision

• Dr. Mahshid Helali Moghadam, Mälardalen University (Co-supervisor)
• Dr. Sten Vercammen, University of Antwerp and Lund University (Co-supervisor)

Selected Publications – Software Engineering Intelligence (AI4SE)

M. Borg, K. Wnuk, B. Regnell, and P. Runeson, Supporting Change Impact Analysis Using a Recommendation System: An Industrial Case Study in a Safety-Critical Context,  IEEE Transactions on Software Engineering, 43(7), pp. 675-700, 2017. Rigorous empirical evaluation of the RecSys ImpRec. Lab experiments combined with deployment in the field.

M. Borg, P. Runeson, and A. Ardö, Recovering from a Decade – A Systematic Mapping of Information Retrieval Approaches to Software Traceability, Empirical Software Engineering, 19(6), pp. 1565-1616, 2014. Remains the most comprehensive overview of IR-based trace recovery.

L. Jonsson, M. Borg, D. Broman et al., Automated Bug Assignment: Ensemble-based Machine Learning in Large Scale Industrial Contexts, Empirical Software Engineering, 21(4), pp. 1533-1578, 2016. The largest study on automated bug assignment in proprietary contexts. Introduces bug ensembles using stacked generalization.

Selected Publications – AI Engineering (SE4AI)

M. Borg, C. Englund, K. Wnuk et al. Safely Entering the Deep: A Review of Verification and Validation for Machine Learning and a Challenge Elicitation in the Automotive Industry, Journal of Automotive Software Engineering, 1(1), 1-19, 2019. An early review article on challenges and opportunities for V&V of ML-based automotive systems.

M. Borg, J. Henriksson, K. Socha et al. Ergo, SMIRK is Safe: A Safety Case for a Machine Learning Component in a Pedestrian Automatic Emergency Brake System, Software Quality Journal, 31:335–403, 2023. A complete safety case for an ML-based component in an ADAS, using the AMLAS framework.

M. Borg, J. Bengtsson, H. Österling, et al. Quality Assurance of Generative Dialog Models in an Evolving Conversational Agent Used for Swedish Language Practice, In Proc. of the 1st Int’l. Conf. on AI Engineering – Software Engineering for AI (CAIN), 2022.

Completed Projects (selected)

• SMILE (2018-2022) – Three consecutive projects on safety analysis and V&V of safety-critical automotive systems that embed machine learning components. Co-applicant and Research Leader. Funded by Vinnova.
• ITEA3 TESTOMAT (2017-2020) – EU project on the next level of test automation. Co-applicant. WP lead. RISE project manager.
• Orion (2015-2019) – Decision support for component-based software engineering of cyber-physical systems. Funded by the Knowledge Foundation.
• EASE (2010-2015) – The Industrial Excellence Centre for Embedded Applications Software Engineering. Completed my PhD thesis on alignment between requirements engineering and software testing.