  Finding important features that contribute to the prediction of neural models
is an active area of research in explainable AI. Neural models are opaque and
finding such features sheds light on a better understanding of their
predictions. In contrast, in this work, we present an inverse perspective of
distractor features: features that cast doubt about the prediction by affecting
the model's confidence in its prediction. Understanding distractors provide a
complementary view of the features' relevance in the predictions of neural
models. In this paper, we apply a reduction-based technique to find distractors
and provide our preliminary results of their impacts and types. Our experiments
across various tasks, models, and datasets of code reveal that the removal of
tokens can have a significant impact on the confidence of models in their
predictions and the categories of tokens can also play a vital role in the
model's confidence. Our study aims to enhance the transparency of models by
emphasizing those tokens that significantly influence the confidence of the
models.
