Miroslaw Staron
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My current research areas include:
Software metrics: Measurement systems in industry and prediction models.
Model driven software development: Industrialization of MDSD; empirical studies in modeling; large-scale model development
Empirical methods in software engineering: with focus on experiments and industrial case studies.
Predicting defect inflow in projects: Predicting number of defects in large and medium sized software project.
Requirements engineering in model based software development projects: supervising doctoral student in this area.
I spend 50% of my work time on field research at Ericsson on the project related to software metrics.
I am also involved in ASIS research project with Volvo Car Corporation.
I cooperate with Volvo Information Technology on research projects related to modeling and MDSD.
Showing 37 publications
Design Patterns Understanding and Use in the Automotive Industry: An Interview Study
Testing, Debugging, and Log Analysis With Modern AI Tools
Research Highlights in Evidence-Based Software Engineering
Focusing on Developers in the Era of AI and ML
Recent Research Into Infrastructure as Code
Comparing Word-Based and AST-Based Models for Design Pattern Recognition
AI, Tech, Energy, and Collaboration
Comparing Anomaly Detection and Classification Algorithms: A Case Study in Two Domains
From the War in Ukraine to Cannabis Use: Exploring a Diverse Set of Papers
Open Source Software: Communities and Quality
Automated Code Review Comment Classification to Improve Modern Code Reviews
Predicting build outcomes in continuous integration using textual analysis of source code commits
Technical Debt Problems and Concerns
Comparing autoencoder-based approaches for anomaly detection in highway driving scenario images
Improving Software Regression Testing Using a Machine Learning-Based Method for Test Type Selection
Software Design Trends Supporting Multiconcern Assurance
(Research) Insights for Serverless Application Engineering
Software engineering and advanced applications conference 2019 – selected papers
Improving Quality of Code Review Datasets – Token-Based Feature Extraction Method
Selective Regression Testing based on Big Data: Comparing Feature Extraction Techniques
Improving Data Quality for Regression Test Selection by Reducing Annotation Noise
Deep learning model for end-to-end approximation of COSMIC functional size based on use-case names
Predicting Test Case Verdicts Using TextualAnalysis of Commited Code Churns
Evolution of technical debt: An exploratory study
Milestone-oriented usage of key performance indicators - An industrial case study
Vetting automatically generated trace links: What information is useful to human analysts?
Comparison of model size predictors in practice
Predicting and evaluating software model growth in the automotive industry
Performance in software development - Special issue editorial
Improving fault injection in automotive model based development using fault bypass modeling
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Showing 4 research projects
Transforming Automotive Architecture with Assistance from AI
Architectural Design and Verification/Validation of Systems with Machine Learning Components
Verification of ISO 26262 Software requirements in safety critical EE-systems (VISEE)