arXiv subjects

Lukas Krodinger

Publications and source records attributed to Lukas Krodinger.

3 recordsLinked to original sources

arXiv2026

Real-World Fault Detection for C-Extended Python Projects with Automated Unit Test Generation

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 and the performance of C. A drawback of this approach is that exceptions raised in C can bypass Python's exception handling and cause the entire interpreter to crash. These crashes are real faults if they occur when calling a public API. While automated test generation should, in principle, detect such faults, crashes in native code can halt the test process entirely, preventing detection or reproduction of the underlying errors and inhibiting coverage of non-crashing parts of the code. To overcome this problem, we propose separating the generation and execution stages of the test-generation process. We therefore adapt Pynguin, an automated test case generation tool for Python, to use subprocess-execution. Executing each generated test in an isolated subprocess prevents a crash from halting the test generation process itself. This allows us to (1) detect such faults, (2) generate reproducible crash-revealing test cases for them, (3) allow studying the underlying faults, and (4) enable test generation for non-crashing parts of the code. To evaluate our approach, we created a dataset consisting of 1648 modules from 21 popular Python libraries with C-extensions. Subprocess-execution allowed automated testing of up to 56.5% more modules and discovered 213 unique crash causes, revealing 32 previously unknown faults.

cs.SE↗

arXiv2025

Constraint-Guided Unit Test Generation for Machine Learning Libraries

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 crucial. However, ML APIs often impose strict input constraints involving complex data structures such as tensors. Automated test generation tools such as Pynguin are not aware of these constraints and often create non-compliant inputs. This leads to early test failures and limited code coverage. Prior work has investigated extracting constraints from official API documentation. In this paper, we present PynguinML, an approach that improves the Pynguin test generator to leverage these constraints to generate compliant inputs for ML APIs, enabling more thorough testing and higher code coverage. Our evaluation is based on 165 modules from PyTorch and TensorFlow, comparing PynguinML against Pynguin. The results show that PynguinML significantly improves test effectiveness, achieving up to 63.9 % higher code coverage.

cs.SE↗

arXiv2025

Combining Type Inference and Automated Unit Test Generation for Python

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 programming languages, such as Python, inhibits test generators, which heavily rely on information about parameter and return types of functions to select suitable arguments when constructing test cases. Since automated test generators inherently rely on frequent execution of candidate tests, we make use of these frequent executions to address this problem by introducing type tracing, which extracts type-related information during execution and gradually refines the available type information. We implement type tracing as an extension of the Pynguin test-generation framework for Python, allowing it (i) to infer parameter types by observing how parameters are used during runtime, (ii) to record the types of values that function calls return, and (iii) to use this type information to increase code coverage. The approach leads to up to 87.8 % more branch coverage, improved mutation scores, and to type information of similar quality to that produced by other state-of-the-art type-inference tools.

cs.SE↗