Neural Code Models: Understanding Causation for Interpretation

Core Concepts

Understanding the causal relationships in Neural Code Models is crucial for accurate interpretation and prediction.

Abstract

The content discusses the importance of understanding causation in Neural Code Models (NCMs) for accurate interpretation. It introduces docode, a method specific to NCMs that explains model predictions based on causal inference. The paper illustrates the practical benefits of docode through a case study on deep learning architectures and NCMs, highlighting sensitivity to code syntax changes and potential biases. The study aims to detect and eliminate confounding bias in NCMs, emphasizing the need for deeper insights into model behavior.

Introduction to Neural Code Models (NCMs) and their increasing use in software engineering tools.
Importance of understanding causation in NCMs for accurate interpretation.
Introduction of docode as a post hoc interpretability method specific to NCMs.
Practical benefits of using docode demonstrated through a case study on deep learning architectures and NCMs.
Insights from the case study showing sensitivity to code syntax changes and potential biases.
Emphasis on detecting and eliminating confounding bias in NCMs.

Stats

"All our NCMs, except for the BERT-like model, statistically learn to predict tokens related to blocks of code with less confounding bias."
"Our observed correlations suggest an influence of interventions related to the insertion or removal of Type II clones in training data."

Quotes

"If neural models fail at justifying their outputs, Can we trust these models? Will these models work in deployment?"
"Researchers require neural models to be robust not only at making predictions but also in the interpretability of those predictions."

Key Insights Distilled From

Toward a Theory of Causation for Interpreting Neural Code Models

by David N. Pal... at arxiv.org 03-20-2024

https://arxiv.org/pdf/2302.03788.pdf

Deeper Inquiries

質問1

因果関係を理解することが、ニューラルコードモデルの信頼性と信頼性向上にどのように役立つか？
Answer 1 here

質問2

docodeなどの因果推論手法を使用する際に関連する潜在的な制限や課題は何ですか？
Answer 2 here

質問3

解釈可能性手法の進歩がソフトウェアエンジニアリングツールの将来的な発展にどのような影響を与える可能性がありますか？
Answer 3 here