AI-generated code can work even when the AI cannot clearly explain it because producing a plausible implementation and reliably understanding every aspect of its behavior are different capabilities. A model may use familiar coding patterns to meet a request or pass the examples it was given without consistently tracking every dependency, branch, hidden assumption, or edge case. That helps explain how code can be useful without making its explanation proof that the code is correct.
How can code work if its explanation is unclear?
Code contains recurring patterns: familiar syntax, library conventions, algorithms, and relationships between names and operations. A language model can draw on those patterns and the prompt context to produce an implementation that works for a particular task or set of inputs. This is a plausible explanation consistent with benchmark findings, not a direct account of the private internal cause of any specific output.
Understanding what code does calls for more than producing a plausible sequence. Someone assessing behavior may need to trace values across functions, follow branches, identify state changes and external assumptions, and consider inputs absent from examples. A system can be good at completing familiar code while being less reliable at those kinds of semantic analysis.
What does the evidence say about the gap?
Semantic understanding is not the same as code completion
A 2026 SemBench study tested program properties including data dependencies, function reachability, dominators, liveness, and dead code. Across 1,000 C programs, the authors created 15,404 semantic questions covering six properties: dead-code statements, data dependencies, function reachability, dominators, dead-code loops, and liveness. They report a substantial gap between static semantic understanding and code-completion capability. Read the SemBench paper.
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A high benchmark score still leaves room for failure
The best of 16 models evaluated in the SemBench study achieved 80.42% accuracy on its semantic questions. The reported failure rates ranged from 19.58% to 86.01%, depending on the tested model and task. These figures describe performance on that benchmark, not the general accuracy of AI-generated code or every current coding assistant.
Some skills overlapped: SemBench reported moderate correlations between function-reachability performance and coding-task success on HumanEval and MBPP (ρ = 0.65 and ρ = 0.73, respectively). The relationship suggests that some semantic ability tracks coding performance, but correlation does not mean the skills are equivalent or that one guarantees the other.
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Explanations can be sensitive to how code is presented
A 2024 study used explainability techniques with eight models and five datasets. It found that tested models could recognize code grammar and structure in some scenarios, but were not robust to changes in input sequences; the authors also warned that data duplication could make earlier evaluations look overly optimistic. The findings concern the models and datasets in that study, not all AI systems. Read the 2024 study.
Why a generated explanation is not proof
An explanation produced after code is generated is not automatically a faithful record of how that code came to be. It may describe visible structure or point to influential tokens, yet still miss a dependency, an untested branch, or an assumption about the environment.
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- “The tool can describe this code” does not establish that the description is complete or correct.
- A persuasive explanation does not prove that the implementation behaves correctly.
- A description of code does not necessarily reveal the model’s internal process for producing it.
These distinctions matter especially when code relies on external APIs, persistent state, concurrency, security-sensitive inputs, or deployment-specific settings. In those cases, apparently reasonable prose can leave important behavior unexamined.
How to evaluate AI-generated code
Treat generated code as a proposal to inspect, not a finished answer. A practical review moves from intended behavior to implementation and then to evidence from tests and analysis.
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- State the requirement and assumptions. Define expected inputs, outputs, error handling, and relevant environment or API constraints.
- Read the implementation. Trace important data through functions, inspect branch conditions and state changes, and check whether dependencies are used as intended.
- Test representative and boundary cases. Include normal inputs, empty or malformed inputs where relevant, and cases likely to exercise alternate branches.
- Use analysis appropriate to the risk. Static analysis, security checks, and human code review can reveal issues that a functional test misses.
- Verify external assumptions. Check the actual API behavior and runtime environment when the code depends on them.
Tests and analysis add evidence; they do not establish correctness for every possible input. A finite test suite can miss an untested condition, and passing tests means only that the code met those tests under their particular conditions.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Can feedback and repair improve the result?
In studied workflows, feeding test results or static-analysis findings back into a model can help it revise generated code. The PROBE experiments reported improved functional correctness after incorporating feedback, with results varying by programming language and task difficulty. That is evidence that feedback can help in those experiments—not a guarantee that an automated repair is safe or complete. Read the testing and static-analysis study; read the PROBE study.
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What these studies do—and do not—show
- SemBench focuses on annotated C programs and selected target functions, and tests a limited set of semantic properties. Its authors also note the role of human verification in semantic annotation.
- The 2024 explainability study covers particular model generations and datasets; its findings should not be treated as a universal ranking of AI tools.
- Benchmark results, test execution, static analysis, human inspection, and similarity to a reference implementation measure different things. A strong result on one does not settle the others.
Together, these studies support a capability gap: a model can produce useful code without reliably accounting for all of its behavior. They do not establish one universal reason for every successful output, nor a single accuracy rate that applies to all languages, models, and production systems.
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