|Sep 26 – Attending DevOps Dagstuhl GI Seminar – going to learn more about performance eng.|
|Sep 12 – Joining the PIMM project today – mobile technology for heavy equipment in a mine
|Aug 29 – New MSc. thesis student started – Iben Lennerstad on a text mining project!|
Dr. Markus Borg is a senior researcher with the Software and Systems Engineering Laboratory at SICS Swedish ICT AB. Contact: markus.borg ~at~ sics.se or @mrksbrg
The goal of my research is to provide actionable decision support to recurring work tasks in software engineering, such as issue management and architectural decision making. My main approach to reach the goals is to tap into the collected wisdom of historical project data, either by offering powerful search and recommendation systems, or by machine learning approaches.
Dr. Markus Borg currently works in the Orion project. When developing a software-intensive product you make choices. What parts should you build, what parts should you buy? Should you use open source software or not? We investigate the decision model practitioners use when weighing different alternatives, and develop COACH – a decision support tool. Orion is a research project led by Blekinge Institute of Technology with SICS Swedish ICT AB and Mälardalen University in partnership funded by the Knowledge Foundation.
During the fall of 2016, I am working on 1) an industrial survey of decision making for selection of software assets and 2) mining quality discussions from StackOverflow. The research will provide a better understanding of how architectural decision are made in industry, and by mining StackOverflow we hope to capture the essence of quality discussions online – thus crowdsourcing architectural decision making.
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, available online.
=> The largest study on automated bug assignment in proprietary contexts. Introduces bug ensembles using stacked generalization.
M. Borg and D. Pfahl, Do Better IR Tools Improve the Accuracy of Engineers’ Traceability Recovery?, In Proc. of the International Workshop on Machine Learning Technologies in Software Engineering, pp. 27-34, 2001.
=> The first use of statistical equivalence testing in software engineering according to Dolado et al. (2014).
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.