2022

Leveraging Causal Inference for Explainable Automatic Program Repair

Wang, Jianzong, Si, Shijing, Zhu, Zhitao et al.

Understand

Deep learning models have made significant progress in automatic program repair.

However, the black-box nature of these methods has restricted their practical applications.
To address this challenge, this paper presents an interpretable approach for program repair based on sequence-to-sequence models with causal inference and our method is called CPR, short for causal program repair.
Our CPR can generate explanations in the process of decision making, which consists of groups of causally related input-output tokens.

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