arXiv:2607.06125v1 Announce Type: cross 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-lev
The increasing sophistication of neural networks makes them viable for complex tasks like decompilation, driving research into their application for modern programming languages and binaries.
Improving neural decompilation could significantly impact software security, reverse engineering, and AI-driven code generation, potentially influencing developer tooling and intellectual property protection.
The explicit focus on evaluating fine-tuning and metrics for neural decompilation specifically for Dart AOT binaries suggests progress in applying AI to this challenging area for modern compiled languages.
- · Software security firms
- · Reverse engineers
- · AI development tool providers
- · Google (as Dart's developer)
- · Malware developers (potentially)
- · Obfuscation tool vendors (potentially)
Further improvements in neural decompilation accuracy for AOT compiled languages will become more common.
Enhanced capabilities for vulnerability research and intellectual property analysis for compiled software will emerge.
The development of 'AI agents' capable of autonomously understanding and modifying low-level compiled code could accelerate.
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Read at arXiv cs.AI