  The code intelligence (CI) models are often black-box and do not offer any
insights on the input features that they learn for making correct predictions.
This opacity may lead to distrust in their prediction and hamper their wider
adoption in safety-critical applications. In recent, the program reduction
technique is widely being used to identify key input features in order to
explain the prediction of CI models. The approach removes irrelevant parts from
an input program and keeps the minimal snippets that a CI model needs to
maintain its prediction. However, the state-of-the-art approaches mainly use a
syntax-unaware program reduction technique that does not follow the syntax of
programs, which adds significant overhead to the reduction of input programs
and explainability of models. In this paper, we apply a syntax-guided program
reduction technique that follows the syntax of input programs during reduction.
Our experiments on multiple models across different types of input programs
show that the syntax-guided program reduction technique significantly
outperforms the syntax-unaware program reduction technique in reducing the size
of input programs. Extracting key input features from reduced programs reveals
that the syntax-guided reduced programs contain more label-specific key input
features and are more vulnerable to adversarial transformation when renaming
the key tokens in programs. These label-specific key input features may help to
understand the reasoning of models' prediction from different perspectives and
increase the trustworthiness to correct classification given by CI models.
