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2026 · Preprint

Faultless: A Program Equivalence Technique for Validating and Evaluating Neural Decompilers

Luke Dramko; Claire Le Goues; Edward Schwartz

Compares neural-decompiler C output with deterministic decompilation or reference functions using symbolic execution and a specialized memory model. It supports both analyst-facing validation and model evaluation without requiring the surrounding codebase.

Scope and limitations

Function-level checks abstract away side-effect ordering and some compiler details; configurable options further relax return, argument and type conditions. The implementation has explicit heap-related soundness limitations and requires compatible loop structure; unsupported C features and context-independent dependency assumptions also limit coverage. Failure to establish equivalence is not proof of inequivalence. Evaluations use filtered realtype and ExeBench pairs, not unrestricted whole-program equivalence.

Why it fits this collection

Adds a concrete validation method for recovered code while exposing the assumptions and unsound cases hidden by an unconditional equivalence label.

Publication and review details

Venue
arXiv
Status
Preprint
Publication / posting date
September 28, 2026
Date source
arXiv v1 submission date; no formal publication venue was verified.
First arXiv submission
September 28, 2026
arXiv
2609.34089
Reviewed arXiv version
v1
DOI
10.48550/arxiv.2609.34089

The three-author order and first-submission date follow arXiv v1. DataCite records the arXiv DOI as findable, registered on 2026-09-29, despite the abstract page’s pending-registration label. The paper is CC BY-SA 4.0. The author-linked public repository was reviewed at 059023d874f4103b5e645a9d6d6ddbdb2bbb6da0; its root license is MIT and README calls the Python/tree-sitter/Z3 implementation a proof of concept. No code or experiments were run.

Sources reviewed

Source review only; experiments were not rerun for this collection. Cite the original authors using the linked paper or BibTeX.

Primary sources

  1. https://arxiv.org/abs/2609.34089
  2. https://arxiv.org/html/2609.34089v1
  3. https://api.datacite.org/dois/10.48550/arxiv.2609.34089
  4. https://github.com/squaresLab/faultless
  5. README.md GitHub · squaresLab/faultless · Revision 059023d
  6. LICENSE GitHub · squaresLab/faultless · Revision 059023d
  7. pyproject.toml GitHub · squaresLab/faultless · Revision 059023d