Evaluating Fine-Tuning and Metrics for Neural Decompilation of Dart AOT Binaries

• URL: http://arxiv.org/abs/2607.06125v1
• Date: Tue, 07 Jul 2026 10:36:12 GMT
• Title: Evaluating Fine-Tuning and Metrics for Neural Decompilation of Dart AOT Binaries
• Abstract summary: We present a systematic empirical study of fine-tuning effectiveness and validity for neural decompilation.<n>We evaluate six finetuned model variants across three base architectures using three metrics: CodeBLEU, compile@k, and pass@k.<n>Crosslingual interference from Swift training is highly significant at 4B (-2.66 pp, p but statistically indistinguishable from zero at 8B, consistent with the scaling hypothesis)
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Neural decompilation is increasingly studied as a code-generation problem, yet its evaluation methodology remains underdeveloped for modern languages. We present a systematic empirical study of fine-tuning effectiveness and metric validity for Dart Ahead-of-Time (AOT) neural decompilation. We evaluate six fine-tuned model variants across three base architectures (4B-8B parameters) using three metrics: CodeBLEU, compile@k, and pass@k on a new 154-task HumanEval-Dart benchmark. Our study yields three principal findings grounded in paired task-level statistical tests. First, no fine-tuning configuration produces a statistically significant pass@k improvement. The sole positive case yields +0.71 pp (McNemar p=0.21), while fine-tuning the strongest base (Qwen3-8B) causes a highly significant regression of -5.65 pp (p<0.001). This capacity-dependent trend is consistent across architectures but needs broader scale sweeps. Second, cross-lingual interference from Swift training is highly significant at 4B (-2.66 pp, p<0.001) but statistically indistinguishable from zero at 8B, consistent with the scaling hypothesis. Third, we demonstrate metric divergence: CodeBLEU and compile@k can improve significantly while pass@k moves in the opposite direction. This has implications for any LLM code generation task where fine-tuning targets superficial similarity. Error analysis reveals assembly sequence length is the strongest predictor of task difficulty (p=0.001), with a capability cliff at 200 instructions. We contribute the HumanEval-Dart benchmark, a Dart-adapted CodeBLEU, and empirical evidence that pass@k must be the primary evaluation metric for neural decompilation.

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