GALA uses hierarchical graph alignment between UI screenshots and code structures to achieve state-of-the-art bug localization in multimodal automated program repair on SWE-bench.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.SE 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Neural Change Prediction generates mutation data to train bidirectional models linking code changes to behavioral effects for any executable program.
citing papers explorer
-
GALA: Multimodal Graph Alignment for Bug Localization in Automated Program Repair
GALA uses hierarchical graph alignment between UI screenshots and code structures to achieve state-of-the-art bug localization in multimodal automated program repair on SWE-bench.
-
Neural Change Prediction: Relating Software Changes to Their Effects and Vice Versa
Neural Change Prediction generates mutation data to train bidirectional models linking code changes to behavioral effects for any executable program.