Coverage Metric

Coverage metrics assess the extent to which a system (e.g., a model, algorithm, or test suite) explores its input space or internal states, aiming to quantify completeness and identify potential weaknesses. Current research focuses on developing and improving these metrics across diverse applications, including evaluating the quality of argument summarization, predicting machine learning model errors by analyzing data dissimilarities, and assessing the thoroughness of deep neural network testing using various structural coverage criteria (e.g., neuron activation patterns). These advancements are crucial for enhancing the reliability and robustness of various systems, particularly in safety-critical domains, by providing quantitative measures of their performance and identifying areas needing further development or testing.