Position: Intelligent Coding Systems Should Write Programs with Justifications

• URL: http://arxiv.org/abs/2508.06017v1
• Date: Fri, 08 Aug 2025 05:04:47 GMT
• Title: Position: Intelligent Coding Systems Should Write Programs with Justifications
• Authors: Xiangzhe Xu, Shiwei Feng, Zian Su, Chengpeng Wang, Xiangyu Zhang,
• Abstract summary: We argue that these systems should not only generate code but also produce clear, consistent justifications that bridge model reasoning and user understanding.<n>We advocate exploring neuro-symbolic approaches for justification generation, where symbolic constraints guide behavior during training and program semantics are enriched through neural representations.
• Score: 9.304020701255093
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Intelligent coding systems are transforming software development by enabling users to specify code behavior in natural language. However, the opaque decision-making of AI-driven coders raises trust and usability concerns, particularly for non-expert users who cannot inspect low-level implementations. We argue that these systems should not only generate code but also produce clear, consistent justifications that bridge model reasoning and user understanding. To this end, we identify two critical justification properties-cognitive alignment and semantic faithfulness-and highlight the limitations of existing methods, including formal verification, static analysis, and post-hoc explainability. We advocate exploring neuro-symbolic approaches for justification generation, where symbolic constraints guide model behavior during training and program semantics are enriched through neural representations, enabling automated consistency checks at inference time.

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