6 papers
Combining Type Inference and Automated Unit Test Generation for Python
Lukas Krodinger, Stephan Lukasczyk, Gordon Fraser
Automated unit test generation is an established research field that has so far focused on statically-typed programming languages. The lack of type information in dynamically-typed…
Quo Vadis, Code Review? Exploring the Future of Code Review
Michael Dorner, Andreas Bauer, Darja Å mite +6
Context: Code review has long been a core practice in collaborative software engineering. As automation becomes increasingly embedded in development workflows, the role and functio…
Real-World Fault Detection for C-Extended Python Projects with Automated Unit Test Generation
Lucas Berg, Lukas Krodinger, Stephan Lukasczyk +4
Many popular Python libraries use C-extensions for performance-critical operations allowing users to combine the best of the two worlds: The simplicity and versatility of Python an…
Constraint-Guided Unit Test Generation for Machine Learning Libraries
Lukas Krodinger, Altin Hajdari, Stephan Lukasczyk +1
Machine learning (ML) libraries such as PyTorch and TensorFlow are essential for a wide range of modern applications. Ensuring the correctness of ML libraries through testing is cr…
Search-based Hyperparameter Tuning for Python Unit Test Generation
Stephan Lukasczyk, Gordon Fraser
Search-based test-generation algorithms have countless configuration options. Users rarely adjust these options and usually stick to the default values, which may not lead to the b…
Mutation Testing via Iterative Large Language Model-Driven Scientific Debugging
Philipp Straubinger, Marvin Kreis, Stephan Lukasczyk +1
Large Language Models (LLMs) can generate plausible test code. Intuitively they generate this by imitating tests seen in their training data, rather than reasoning about execution…