DeepCover: Advancing RNN Test Coverage and Online Error Prediction using State Machine Extraction

Recurrent neural networks (RNNs) have emerged as powerful tools for processing sequential data in various fields, including natural language processing and speech recognition. However, the lack of explainability in RNN models has limited their interpretability, posing challenges in understanding their internal workings. To address this issue, this paper proposes a methodology for extracting a state machine (SM) from an RNN-based model to provide insights into its internal function. The proposed SM extraction algorithm was assessed using four newly proposed metrics: …

challenges coverage cs.lg data error explainability extraction fields interpretability issue language language processing machine natural natural language natural language processing networks neural networks prediction processing recognition recurrent neural networks rnn speech speech recognition state test tools understanding