6 papers
Continuous Autonomous Refactoring: A Research Roadmap for AI-Driven Code Quality Maintenance
Xin Sun, Daniel Ståhl, Kristian Sandahl +1
Large language models have shown promising capabilities in code refactoring, but existing approaches remain limited to method-level tasks. In this paper, we envision LLM-based refa…
Beyond Resolved Rate: A Non-Functional Quality Study
Xin Sun, Daniel Ståhl, Kristian Sandahl +1
Repository-level coding benchmarks typically measure progress in model capability by comparing the resolved rates of later and earlier models. However, this focus overlooks whether…
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…
Quality Assurance of LLM-generated Code: Addressing Non-Functional Quality Characteristics
Xin Sun, Daniel Ståhl, Kristian Sandahl +1
In recent years, large language models have been widely integrated into software engineering workflows, supporting tasks like code generation. While prior evaluations focus on func…
A Large Language Model Approach to Identify Flakiness in C++ Projects
Xin Sun, Daniel Ståhl, Kristian Sandahl
The role of regression testing in software testing is crucial as it ensures that any new modifications do not disrupt the existing functionality and behaviour of the software syste…