Searching for Quality: Genetic Algorithms and Metamorphic Testing for Software Engineering ML

More machine learning (ML) models are introduced to the field of Software Engineering (SE) and reached a stage of maturity to be considered for real-world use; But the real world is complex, and testing these models lacks often in explainability, feasibility and computational capacities. Existing research introduced meta-morphic testing to...

HasBugs - Handpicked Haskell Bugs

We present HasBugs, an extensible and manually-curated dataset of real-world 25 Haskell Bugs from 6 open source repositories. We provide a faulty, tested, and fixed version of each bug in our dataset with reproduction packages, description, and bug context. For technical users, the dataset is meant to either help researchers adapt techniques...

PropR: Property-Based Automatic Program Repair

Automatic program repair (APR) regularly faces the challenge of overfitting patches — patches that pass the test suite, but do not actually address the problems when evaluated manually. Currently, overfit detection requires manual inspection or an oracle making quality control of APR an expensive task. With this work, we want to introduce...

Assessing Robustness of ML-Based Program Analysis Tools using Metamorphic Program Transformations

Metamorphic testing is a well-established testing technique that has been successfully applied in various domains, including testing deep learning models to assess their robustness against data noise or malicious input. Currently, metamorphic testing approaches for machine learning (ML) models focused on image processing and object recognition...