|May 27 – ICSE18: SEIS paper. Organizing SER&IP and RET. Industry forum panelist. Booth crew.|
|May 21 – Organized the LU Robocode Rumble 2018 – This year’s teaching is almost done.|
|Apr 25 – Elected to the board of Swedsoft – a non-profit organization supporting Swedish SE|
Dr. Markus Borg is a senior researcher with the Software and Systems Engineering Laboratory at RISE SICS AB and an adjunct senior lecturer at Lund University. He is also a board member of Swedsoft. Contact: markus.borg ~at~ ri.se or @mrksbrg
The goal of my research is to support successful engineering of software-intensive systems. My main contributions deal with tapping into the collected wisdom of historical project data to facilitate machine learning for actionable decision support in defect management, for example bug assignment and change impact analysis. Currently, I’m in a transition from “machine learning for software engineering” to “software engineering for machine learning” – I want to evolve software engineering practices to match the needs when developing the data-driven systems of the future. At the moment, I work toward my goals in three research projects:
The TESTOMAT project is all about test automation. While being an acknowledged best practice for years, test automation needs to go beyond repeated execution of test cases in a continuous integration context. In this ITEA3 project, 40 partners across Europe explore how to bring test automation to the next level, studying topics such as test case generation, automated test result analysis, test suite assessment and maintenance, and test automation infrastructure. TESTOMAT develops a novel test automation improvement model, defining key improvement areas and measurable improvement steps.
The SMILE project (Safety analysis and verification/validation of MachIne LEarning based systems), explores the software side of increased autonomy in the automotive domain. In the near future, vehicles will rely on deep learning to implement perception. However, safe behavior cannot be assured using traditional software engineering approaches, e.g., code review and complete code coverage testing. In this FFI Machine Learning project, RISE develops a safety cage around a deep learning component in collaboration with Volvo Cars, AB Volvo, QRTECH, and Semcon.
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 and P. Runeson, Changes, Evolution and Bugs – Recommendation Systems for Issue Management, In Recommendation Systems in Software Engineering, pp. 477-509, 2014.
=> A book chapter on bug duplicate detection and assisted change impact analysis.
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.
=> 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.
M. Borg, TuneR: A Framework for Tuning Software Engineering Tools with Hands-on Instructions in R, Journal of Software: Evolution and Process, 28(6), pp. 426-459, 2016.
=> A tutorial paper on how to use “design of experiments” to tune parameters of software engineering tools.