WheaCha: A Method for Explaining the Predictions of Models of Code

Understand

Attribution methods have emerged as a popular approach to interpreting model predictions based on the relevance of input features.

• Although the feature importance ranking can provide insights of how models arrive at a prediction from a raw input, they do not give a clear-cut definition of the key features models use for the prediction.
• In this paper, we present a new method, called WheaCha, for explaining the predictions of code models.
• Although WheaCha employs the same mechanism of tracing model predictions back to the input features, it differs from all existing attribution methods in crucial ways.