Home

LATEST NEWS:
Apr 13 – Going to ICSE! Will present papers at TechDebt and MO2RE.
Apr 11 – Attended a workshop in Gothenburg on systems eng. of learning-enabled systems.
Mar 26 – WASP research proposal granted – Trustworthy AI refactoring!

Dr. Markus Borg is a senior researcher at the intersection of software engineering and applied artificial intelligence. He is a principal researcher at CodeScene and an adjunct associate professor at Lund University. Markus serves on the editorial board of Empirical Software Engineering and is a department editor for IEEE Software.

Contact him at markus.borg@codescene.com or @mrksbrg

My goal is to support the successful engineering of software and data-intensive systems. While my software engineering interests are broad, most of my work relates to machine learning, i.e., my research interests span both 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 embed machine learning components. I am particularly interested in development mandated by automotive safety standards and the expected 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)
  • Dimitris Paraschakis, Postdoc, Theca Systems and Lund University, 2023- (Co-supervisor)
  • Rushali Gupta, PhD Student, Lund University, 2024- (Co-supervisor)

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.

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.