Do not trust AI-generated decompiled code because it looks plausible or compiles. Treat it as an unverified draft and require independent evidence: a reproducible baseline, static comparison with the original binary, a pinned toolchain, object or image diffs, deterministic behavioral tests, and human review. A byte-identical build is the strongest evidence of a matching reconstruction; a behaviorally equivalent port is a different and narrower claim.
What “correct” means in a game decompilation
Verification starts by defining the claim you are making. A matching decompilation attempts to reproduce the original instructions, data layout, and link result with the original or closely matched compiler and build settings. A semantic port attempts to preserve observable behavior while allowing different source code, instructions, libraries, timing, or memory layout. Both can be valid, but evidence for one does not prove the other.
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| Claim | Best evidence | What it does not prove |
|---|---|---|
| Function or object matches the original | Matching compiler, flags, assembly/object comparison, and preferably identical bytes | That the surrounding game, assets, or platform services are correct |
| Port behaves equivalently | Deterministic differential tests, replay traces, save-state comparisons, and game-level tests | Byte identity or identical performance on every platform |
| Code is safe to ship | Human review, sanitizer and fuzz results, release-style installation and hardware checks, and reproducible provenance | That it is legally authorized for public distribution |
There is no authoritative percentage showing that AI-generated decompiled game code is correct. Do not substitute unrelated decompiler or smart-contract benchmarks for evidence about your title.
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Work from a lawfully obtained copy and write down what you are permitted to do before analyzing code or assets. Record the target platform, region, executable or ROM version, build hash, and intended use (private interoperability work, preservation, modding, internal porting, or public distribution).
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In the United States, 17 U.S.C. §1201(f) describes a narrow interoperability exception for a lawful copy when analysis identifies interoperability elements that were not previously readily available and the acts do not infringe. It is not blanket permission to publish a game’s code, distribute extracted assets, or release a commercial port. Other countries apply different rules. Obtain jurisdiction-specific legal advice before a public release, and keep proof of provenance for every binary and asset.
Separate the legal scopes
| Activity | Questions to answer |
|---|---|
| Private interoperability analysis | Is the copy lawful, and are the examined elements genuinely needed for interoperability? |
| Internal preservation | Who owns the source media, and where may reconstructed code and extracted assets be stored? |
| Modding | Does the platform license permit modified executables, and can the mod avoid redistributing protected assets? |
| Public or commercial distribution | Are code, assets, trademarks, online services, and platform requirements separately cleared in each jurisdiction? |
A technically accurate reconstruction can still be unsuitable for release if its distribution rights are unclear. Keep legal scope in the project record rather than assuming that a personal analysis permits a public port.
2. Freeze a reproducible baseline before using an AI agent
The baseline is the reference against which every proposed change is measured. Preserve the original executable or ROM without modification and hash it with a documented algorithm. Archive the linker map, symbols, compiler family and version, compiler flags, SDK, libraries, asset-extraction inputs, emulator or hardware configuration, and build scripts when available.
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Build the unmodified project first. If it does not build, stop and repair that problem before asking an agent to reconstruct functions. The practical rule from the Reversed with AI guide, updated 14 October 2026, is: “Get the project building to a byte-identical image before you involve an agent.” Without that baseline, a later difference could come from the environment rather than the generated code.
- Store hashes for the original image, extracted assets, toolchain archives, and each generated patch.
- Use a clean checkout and a locked dependency set for every comparison.
- Record emulator version, firmware, timing mode, display mode, controller mapping, and save-state format.
- Keep an untouched reference copy; never overwrite it with an experimental build.
- Record the image layout and any fields that legitimately vary between builds, such as timestamps or signatures.
Run a clean rebuild more than once. If identical inputs produce different output, find the nondeterministic step before evaluating an AI change. Otherwise an apparently successful diff may only reflect build noise.
3. Challenge the draft with static analysis
Use a disassembler and decompiler to test the model’s claims, not to confirm them. Ghidra is a software reverse-engineering framework created and maintained by the National Security Agency Research Directorate. Its disassembly, decompilation, graphing, scripting, and automation features can expose contradictions in an AI explanation.
Check control flow and calls
- Compare every conditional branch, fall-through path, loop exit, and error path with the disassembly.
- Verify that calls point to the claimed targets and that cross-references account for all callers and data users.
- Look for unreachable blocks, missing switch cases, incorrect tail calls, and assumptions about inlining.
- Trace jump tables, indirect calls, callbacks, exception paths, and state-machine transitions rather than checking only the main path.
Check the machine-level contract
- Confirm stack offsets, register preservation, argument order, return registers, and calling convention against the platform ABI.
- Check integer width, signedness, pointer arithmetic, structure packing, alignment, endianness, and volatile or memory-mapped accesses.
- Compare floating-point mode, rounding, exception behavior, and conversions; apparently harmless changes can alter physics or timing.
- Verify global addresses, overlay boundaries, DMA buffers, interrupt-visible state, and aliasing assumptions where the platform uses them.
Names, comments, and inferred types produced by a model are hypotheses. Accept them only when references, data-flow evidence, or runtime observations support them. A confident explanation is not an observation from the original program.
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4. Reconstruct the original toolchain conditions
Matching decompilation needs the game’s original compiler at the right version and flags, or the closest reproducible substitute. The decomp.wtf documentation notes that compiler and flag differences can prevent a match even when source-level behavior appears equivalent.
| Setting | Why it changes the result | What to record |
|---|---|---|
| Compiler family and exact version | Different instruction selection, register allocation, and undefined-behavior handling | Version string, binaries, patches, and host platform |
| Optimization level and inlining | Changes control flow, function boundaries, and scheduling | All optimization and link-time options |
| Structure packing and ABI | Changes offsets, padding, alignment, and calls | Target ABI, packing pragmas, language mode |
| Floating-point mode | Changes precision, rounding, and instruction sequences | Hardware mode and compiler switches |
| Linker order and libraries | Changes addresses, dead-code elimination, and library code | Linker script, object order, SDK and library versions |
Containerize or otherwise lock the environment where practical. A clean rebuild should produce the same image hash before you evaluate an AI patch. If the exact compiler cannot be recovered, record the substitute and change the acceptance claim to semantic equivalence; do not quietly call a different toolchain an exact match.
5. Compile in progressively stronger stages
- Parse and type-check. Catch syntax errors, undeclared symbols, incompatible widths, and impossible casts.
- Compile the smallest unit. Build the candidate function or object with warnings enabled. Treat new warnings as failures until reviewed.
- Inspect assembly. Compare branches, loads, stores, calls, constants, register use, and instruction ordering with the original.
- Compare object files. Use section, relocation, symbol, and machine-code diffs to isolate the first divergence.
- Compare the complete image. Rebuild with identical linker inputs and compare the final image, excluding only documented nondeterministic fields.
Start with the smallest unit that can expose a problem, then move outward. A function-level mismatch is easier to diagnose before inlining, library code, and linker layout add thousands of unrelated differences. Keep both the raw diff and a human-readable explanation of the first divergence.
Byte identity is strong evidence for a matching reconstruction, but it is not automatically proof that every external service, asset pipeline, or platform integration is correct. Conversely, a non-identical image can still implement equivalent behavior; label that result as semantic equivalence rather than a match.
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Execute the original and candidate with identical deterministic inputs. Use save states, replay files, scripted controller traces, fixed random seeds, and controlled clocks where the platform permits. Capture outputs at checkpoints rather than relying on a human saying that the game “feels the same.”
Function-level tests
- Test normal, boundary, empty, maximum-size, and malformed inputs.
- Compare return values, output buffers, flags, error paths, and state mutations.
- Check behavior across repeated calls, reset conditions, and interrupted operations.
- Compare side effects such as allocation order, queue contents, timers, and persistent flags when those affect later frames.
Game-level tests
- Boot and initialization, including missing or damaged data.
- Game-loop timing, frame pacing, input polling, pause, and resume.
- Collision, animation, rendering, audio mixing, and deterministic random behavior.
- Save, load, migration, corrupt-save handling, and memory-boundary cases.
- Platform services, controller layouts, display modes, suspend/resume, and network or multiplayer integration when supported.
When a trace diverges, preserve the smallest reproducing input and state as a regression test. Compare intermediate state, not only the final screen: the first differing frame, memory region, event, or output buffer usually identifies the faulty reconstruction. A passing end-to-end scenario can conceal a wrong branch that the chosen inputs never exercise.
7. Add fuzzing and runtime safety checks
Unit tests cover known cases; fuzzing explores combinations a model and developer may not anticipate. Generate malformed files, extreme dimensions, truncated packets, invalid save data, unusual controller sequences, and boundary coordinates appropriate to the function. Keep every crash, hang, timeout, and divergent output with its seed and input.
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Run AddressSanitizer or an equivalent memory-safety tool where the target environment and performance budget permit it. Also check undefined behavior, integer overflow, out-of-bounds access, use-after-free, uninitialized reads, and data races. Sanitizer-clean execution does not prove equivalence, but a report is a release blocker until understood and fixed or formally accepted.
Run safety instrumentation in a host or diagnostic build when it cannot run on the original hardware, then repeat the relevant scenario on the target. Document which checks were unavailable because of platform constraints; absence of a tool result is not a clean result.
8. Choose a local or hosted AI workflow deliberately
AI assistance introduces an operational decision separate from code correctness: whether binaries, decompiler output, prompts, and generated patches leave your environment. In a local workflow, the executable, symbols, and intermediate traces can remain on project-controlled machines. In a hosted workflow, confirm what is uploaded, whether prompts or telemetry are retained, who can access them, and whether the service permits the material under your rights and agreements.
| Workflow | Advantages | Questions before use |
|---|---|---|
| Local model or local tooling | Keeps sensitive binaries and traces within the controlled environment | Can the team reproduce the model version, dependencies, and hardware needed for review? |
| Hosted model | May provide more capable generation or easier collaboration | What data is transmitted, retained, logged, or used for service improvement, and is that allowed? |
Whichever route you choose, store the model name and version when available, the prompt, the source address range, and the exact generated patch. A hosted response that cannot be retrieved later is not sufficient provenance for a release decision.
9. Distinguish function-level evidence from whole-game readiness
A reconstructed function can pass object comparison while the port still fails because of an incorrect asset, linker script, timing source, save format, or platform wrapper. Conversely, a complete game can appear playable while a rarely used function contains undefined behavior. Use two separate gates.
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- Function or object gate: static control-flow and ABI review, unit and boundary tests, assembly or object diff, and a recorded claim of byte identity or semantic equivalence.
- Whole-game gate: full-image comparison where applicable, deterministic replays, clean installation, save migration, performance, device handling, service integration, and supported-hardware coverage.
Link each whole-game failure back to the smallest affected function or subsystem when possible. This prevents a green menu test from being mistaken for proof that rendering, collision, audio, or persistence matches the original.
10. Apply release-style gates before shipping
Microsoft’s certification guidance describes build verification tests (BVTs) as a suite used to determine a title’s readiness for Xbox Certification testing. Apply the same gate concept even when your target is another platform: each item should have a reproducible pass or fail result, an owner, and the build hash tested.
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- Clean-install launch on every supported operating system, architecture, and hardware tier.
- Dependency detection and clear handling of missing runtimes, permissions, and files.
- Controller discovery, remapping, keyboard support, display resolutions, refresh rates, and fullscreen transitions.
- Save creation, upgrade, rollback, corruption handling, and migration from the supported original version.
- Suspend, resume, sleep, focus loss, device removal, and repeated relaunch.
- Performance, memory use, loading behavior, audio devices, and thermal or battery-sensitive modes where relevant.
- Multiplayer, online services, authentication, and offline fallback if the port includes them.
- Repeatability on supported hardware, with logs and artifacts retained for each run.
Run these checks on a clean machine, not only on the developer workstation. Record the operating-system build, drivers, firmware, display configuration, controller model, and build hash so a later pass can be reproduced.
11. Review provenance and explainability
AI assistance does not transfer responsibility for the result. The psxrecomp contribution guidance states: “Using AI does not reduce your responsibility for the result: review, understand, and test any code before submitting it.” Require a reviewer who can explain each accepted function and point to the evidence supporting it.
For every generated change, retain:
- Model name and version, if available, and the prompt or task description.
- Source function or address range, generated patch, and human edits.
- Compiler output, warnings, assembly and object diffs, final image comparison, and hashes.
- Test inputs, replay or save-state files, sanitizer and fuzz results, and failure history.
- Reviewer identity, decision, date, unresolved assumptions, and the exact build configuration.
Reject code that cannot be explained or whose verification cannot be reproduced by another contributor from the archived inputs. Provenance also helps you remove model-invented comments and names that later turn out to be wrong. When a reviewer accepts a semantic substitute instead of a matching function, record that decision explicitly and identify the unverified binary differences.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Matching decompilation or semantic port?
| Choose matching decompilation when… | Choose semantic port when… |
|---|---|
| You need preservation, exact patches, original timing, or compatibility with existing binary interfaces. | You are moving to a new architecture, graphics API, operating system, or input model. |
| The original compiler, SDK, linker behavior, and build inputs can be recovered or closely matched. | The old toolchain cannot be reproduced and equivalent behavior is the practical goal. |
| Object or full-image diffs are a meaningful acceptance criterion. | Deterministic behavior, user-visible output, and platform tests are the acceptance criteria. |
| Small instruction-level changes could affect timing, hardware interaction, or compatibility. | New platform services, accessibility, resolution, or controller requirements justify deliberate redesign. |
Static evidence and dynamic evidence answer different questions. Disassembly, cross-references, types, and ABI checks tell you whether the draft explains the machine code. Differential execution, replay tests, fuzzing, and sanitizers tell you whether the implementation behaves safely under exercised conditions. Neither category replaces the other.
Do not call a semantic port “byte compatible,” and do not call a passing menu test “verified.” State the exact scope: for example, “the inventory parser is behaviorally equivalent for the tested input set” or “the rebuilt object is byte-identical under the recorded compiler and linker configuration.”
Common failure modes
- Trusting plausibility: fluent comments and sensible variable names can conceal an inverted condition or wrong signedness.
- Comparing only screenshots: visual output may match while timers, memory, audio state, or save data diverge.
- Changing several variables at once: an updated compiler, SDK, and generated function make the first cause of a mismatch impossible to identify.
- Ignoring platform ABI details: a function can pass desktop tests and fail on the target because of alignment, register use, endianness, or memory-mapped behavior.
- Overlooking nondeterminism: timestamps, link order, uninitialized data, and uncontrolled clocks can create false diffs or hide real ones.
- Stopping at a clean build: syntax and type correctness say nothing about equivalence, safety, rights, or release readiness.
- Failing to preserve failures: a one-off crash that is not saved with its input and seed will likely return after another change.
A practical acceptance checklist
- Rights, target version, region, platform, and permitted distribution are documented.
- The untouched original and all inputs are hashed and archived.
- The baseline project builds reproducibly before AI changes.
- Disassembly, ABI, data layout, and cross-references support the reconstructed claims.
- Compiler, flags, SDK, libraries, linker order, and floating-point settings are pinned.
- Syntax, warnings, assembly, object, and full-image comparisons are complete.
- Deterministic differential tests cover normal and boundary behavior.
- Fuzzing and sanitizers have been run where technically possible.
- Local or hosted AI data handling is approved and recorded.
- Function-level and whole-game acceptance claims are stated separately.
- Clean-install, hardware, save, input, display, suspend/resume, and service gates pass.
- Every accepted function has a reviewer, evidence trail, and reproducible artifacts.
Frequently Asked Questions
Can a decompiler output be trusted if the game compiles?
No. Compilation proves only that the candidate satisfies the new build’s syntax and type rules. Compare it with the original binary and run deterministic differential tests before treating it as verified.
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Is byte-identical output required for a game port?
Only when your claim is a matching decompilation or binary-level compatibility. A semantic port can use different instructions, but it must be labeled accordingly and supported by behavioral and release tests.
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What if the original compiler is unavailable?
Document that limitation, use the closest reproducible toolchain, and focus acceptance on semantic equivalence, deterministic traces, safety checks, and platform coverage rather than claiming an exact match.
Should AI-generated comments and function names be kept?
Only when static references or runtime evidence support them. Otherwise treat them as hypotheses and replace or qualify them so later contributors do not mistake guesses for facts.
Does a private interoperability project have the same rights as a public port?
No. Scope matters. A narrow U.S. interoperability exception under 17 U.S.C. §1201(f) does not grant blanket permission to distribute code, assets, or a commercial release. Obtain jurisdiction-specific legal advice.
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Check its callers, boundary and malformed inputs, return values, side effects, reset behavior, and interactions with timing, memory, saves, rendering, audio, and platform services. Then run whole-game tests as a separate gate.
Is static analysis enough to verify AI-decompiled code?
No. Static analysis challenges control flow, data layout, ABI use, and call targets. Differential execution, replay traces, fuzzing, sanitizers, and release-style tests provide independent behavioral and safety evidence.
Can a hosted AI service receive a game binary?
Only after confirming its upload, retention, telemetry, access, and data-use terms and ensuring they fit your rights and agreements. Record what was sent and preserve the model response and generated patch.
How should a failed comparison be handled?
Keep the original input, state, seed, logs, and smallest reproducing case as a regression test. Identify the first differing instruction, state region, frame, or output rather than judging only the final screen.
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AI can accelerate reconstruction, but it cannot supply proof. Establish a lawful, hashed baseline; challenge every claim against disassembly and the ABI; reproduce the compiler and flags; compare objects or the full image; run deterministic differential, fuzz, and safety tests; then pass release gates and human review with complete provenance. Ship only the claim your evidence actually supports.
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