88 citations · 205 across the 9 of their papers we have counts for
12 papers
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…
Quo Vadis, Code Review? Exploring the Future of Code Review
Michael Dorner, Andreas Bauer, Darja Šmite +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…
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…
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…
SBFT Tool Competition 2024 -- Python Test Case Generation Track
Nicolas Erni, Al-Ameen Mohammed Ali Mohammed, Christian Birchler +3
Test case generation (TCG) for Python poses distinctive challenges due to the language's dynamic nature and the absence of strict type information. Previous research has successful…
Pynguin: Automated Unit Test Generation for Python
Stephan Lukasczyk, Gordon Fraser
Automated unit test generation is a well-known methodology aiming to reduce the developers' effort of writing tests manually. Prior research focused mainly on statically typed prog…
An Empirical Study of Automated Unit Test Generation for Python
Stephan Lukasczyk, Florian Kroiß, Gordon Fraser
Various mature automated test generation tools exist for statically typed programming languages such as Java. Automatically generating unit tests for dynamically typed programming…
Improving Readability of Scratch Programs with Search-based Refactoring
Felix Adler, Gordon Fraser, Eva Gründinger +5
Block-based programming languages like Scratch have become increasingly popular as introductory languages for novices. These languages are intended to be used with a "tinkering" ap…
An Empirical Study of Flaky Tests in Python
Martin Gruber, Stephan Lukasczyk, Florian Kroiß +1
Tests that cause spurious failures without any code changes, i.e., flaky tests, hamper regression testing, increase maintenance costs, may shadow real bugs, and decrease trust in t…
Automated Unit Test Generation for Python
Stephan Lukasczyk, Florian Kroiß, Gordon Fraser
Automated unit test generation is an established research field, and mature test generation tools exist for statically typed programming languages such as Java. It is, however, sub…