5 papers
Data-aware Static Analysis: Improving Detection of Semantic Faults in Machine Learning Code Using Data Characteristics
Willem Meijer, Kristian Sandahl, Dániel Varró
Semantic faults specific to the use of machine learning models are a common problem for machine learning developers, causing suboptimal predictions, high computational cost, or inc…
Are We Lost in the Woods? Detecting Silent Semantic Faults for Random Forest Classifiers with Data-informed Static Analysis
Willem Meijer, Louis Ohl, Kristian Sandahl +1
While machine learning (ML) software necessitates effective quality assurance, ML engineers still encounter silent semantic faults, such as imbalanced datasets, that degrade predic…
Why do Machine Learning Notebooks Crash? An Empirical Study on Public Python Jupyter Notebooks
Yiran Wang, Willem Meijer, José Antonio Hernández López +2
Jupyter notebooks have become central in data science, integrating code, text and output in a flexible environment. With the rise of machine learning (ML), notebooks are increasing…
Ecosystem-wide influences on pull request decisions: insights from NPM
Willem Meijer, Mirela Riveni, Ayushi Rastogi
The pull-based development model facilitates global collaboration within open-source software projects. However, whereas it is increasingly common for software to depend on other p…
Experimental evaluation of architectural software performance design patterns in microservices
Willem Meijer, Catia Trubiani, Aldeida Aleti
Microservice architectures and design patterns enhance the development of large-scale applications by promoting flexibility. Industrial practitioners perceive the importance of app…