Rate My Professor Morgan Ericsson

Ericsson's research specializes in automated methods for determining software quality and functionality, initially through metrics like cohesion and cyclomatic complexity for source code and technical documentation. His current focus involves data analysis and measurement for software artifacts, extracting quality assessments from large datasets, and applying them to complex systems using machine learning, such as in forest attribute maps, log inspection, and code-to-architecture mapping. He acts as research coordinator in the Linnaeus University Centre for Data Intensive Sciences and Applications (DISA), and participates in groups like Data Intensive Software Technologies and Applications (DISTA) and the High-Performance Computing Center (HPCC). Ongoing projects include HPC for SME and In-line visual inspection using unsupervised learning; completed ones feature Development of a Master Programme in Computer Science and Software Technology for Self-Adaptive Systems. Key publications include Improving national forest attribute maps of Sweden with machine learning (Science of Remote Sensing, 2026), Metrics As Scores: A Tool- and Analysis Suite and Interactive Application for Exploring Context-Dependent Distributions (Journal of Open Source Software, 2023), The Design and Implementation of a Software Infrastructure for IQ Assessment (International Journal of Information Quality, 2012), and A Visualization-based Approach to Present and Assess Technical Documentation Quality (Electronic Journal of Information Systems Evaluation, 2011).