4 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…
Hierarchical Evaluation of Software Design Capabilities of Large Language Models of Code
Mootez Saad, Boqi Chen, José Antonio Hernández López +2
Large language models (LLMs) are being increasingly adopted in the software engineering domain, yet the robustness of their grasp on core software design concepts remains unclear.…
SeBERTis: A Framework for Producing Classifiers of Security-Related Issue Reports
Sogol Masoumzadeh, Yufei Li, Shane McIntosh +2
Monitoring issue tracker submissions is a crucial software maintenance activity. A key goal is the prioritization of high risk, security-related bugs. If such bugs can be recognize…