Mind the Gaps: From Failure Attribution to Closed-Form Repair of Code Language Models
Code language models must be maintained like the software around them: when a library evolves, a model keeps writing the interface that it saw during training. Repairing the model itself lets one correction reach all downstream uses. Existing repair methods attribute a failure to neurons, select the highest-ranked ones, and apply a generic update. This pipeline assumes that the attributed neurons are the ones to patch and that a generic update fits every failure, and neither assumption has been examined. We examine both on executable API evolution tasks in Python and Rust with three code models and identify two gaps. The targeting gap separates the neurons that failure attribution targets...
| Authors | Jian Gu, Hongyu Zhang, Chunyang Chen et al. |
|---|---|
| Primary category | cs.SE |
| Published | 4 October 2026 |
| Last updated at source | 4 October 2026 |
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