DiffWatch: Watch Out for the Evolving Differential Testing in Deep Learning Libraries

Testing deep learning libraries is ultimately important for ensuring the quality and safety of many deep learning applications. As differential testing is commonly used to help the creation of test oracles, its maintenance poses new challenges. In this tool demo paper, we present DiffWatch, a fully automated tool for Python, which identifies differential test practices in DLLs and continuously monitors new changes of external libraries that may trigger the updates of the identified differential tests.Our evaluation on four DLLs demonstrates that DiffWatch can detect differential testing with a high accuracy. In addition, we demonstrate usage examples to show DiffWatch’s capability of monitoring the development of external libraries and alert the maintainers of DLLs about new changes that may trigger the updates of differential test practices. In short, DiffWatch can help developers adequately react to the code evolution of external libraries. DiffWatch is publicly available and a demo video can be found at https://www.youtube.com/watch?v=gR7m5QQuSqE.