EAGLE
Jiannan Wang, Thibaud Lutellier, Shangshu Qian, Hung Viet Pham, Lin Tan
2022Proceedings of the 44th International Conference on Software Engineering31 citationsDOIOpen Access PDF
Abstract
Testing deep learning (DL) software is crucial and challenging. Recent approaches use differential testing to cross-check pairs of implementations of the same functionality across different libraries. Such approaches require two DL libraries implementing the same functionality, which is often unavailable. In addition, they rely on a high-level library, Keras, that implements missing functionality in all supported DL libraries, which is prohibitively expensive and thus no longer maintained.
Topics & Concepts
Computer scienceEagleImplementationSoftwareDeep learningArtificial intelligenceSoftware engineeringProgramming languagePaleontologyBiologySoftware Testing and Debugging TechniquesAdversarial Robustness in Machine LearningMachine Learning and Data Classification