7 papers · 1 filter
Vibe Coding: An Experiment with Test-Driven Development
Moritz Mock, Barbara Russo
Context: Conversational Large Language Models (CLLMs) can automatically generate code by collaborating with users through natural language. However, poor collaboration can lead to…
Cross-Domain Evaluation of Transformer-Based Vulnerability Detection on Open & Industry Data
Moritz Mock, Thomas Forrer, Barbara Russo
Deep learning solutions for vulnerability detection proposed in academic research are not always accessible to developers, and their applicability in industrial settings is rarely…
Leveraging multi-task learning to improve the detection of SATD and vulnerability
Barbara Russo, Jorge Melegati, Moritz Mock
Multi-task learning is a paradigm that leverages information from related tasks to improve the performance of machine learning. Self-Admitted Technical Debt (SATD) are comments in…
Optimizing Deep Learning Models to Address Class Imbalance in Code Comment Classification
Moritz Mock, Thomas Borsani, Giuseppe Di Fatta +1
Developers rely on code comments to document their work, track issues, and understand the source code. As such, comments provide valuable insights into developers' understanding of…
MADE-WIC: Multiple Annotated Datasets for Exploring Weaknesses In Code
Moritz Mock, Jorge Melegati, Max Kretschmann +2
In this paper, we present MADE-WIC, a large dataset of functions and their comments with multiple annotations for technical debt and code weaknesses leveraging different state-of-t…
Where do developers admit their security-related concerns?
Moritz Mock, Thomas Forrer, Barbara Russo
Developers use different means to document the security concerns of their code. Because of all of these opportunities, they may forget where the information is stored, or others ma…