Evaluating Neural Decompilation of Dart AOT Binaries: Fine-Tuning, Metric Validity, Specification Leakage, and Reliability
We present an execution-based evaluation of neural decompilation for Dart ahead-of-time binaries and an audit of what its scores measure. Across six archived adapter-baseline comparisons, paired tests of pass@k at k = 1, 5, and 10, with Holm adjustment over 18 endpoints, identify functional regressions in both Qwen3-8B adapters at every k. The other four comparisons are inconclusive. On 141 reference-certified, contract-valid tasks, three independently trained graph-prefix systems score the same candidates. Best CodeBLEU has modest association with pass@10 ($ρ$ = .218-.246), compile@10 has weak association ($ρ$ = .072-.082), and only 21.0-23.3% of compiling candidates pass. A paired singl...
| Authors | Raafat Abualazm, Ayman AboElhassan, Amr G. Wassal |
|---|---|
| Primary category | cs.SE |
| Published | 7 July 2026 |
| Last updated at source | 1 October 2026 |
Silytics shows public metadata from arXiv and links to the original. Information may be incomplete or out of date. Last refreshed 17 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.