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What does the evidence say about AI-generated software?
The available results measure different things: passing unit tests, reviewers’ ratings, security weaknesses in sampled code, repair of known vulnerabilities, and developer activity. They come from different settings and publishers, so they should not be combined into one score for “AI reliability.”
| Study | Setting and sample | Reported result | What it can tell you |
|---|---|---|---|
| GitHub, “Does GitHub Copilot improve code quality?” (2024; updated 2025) | 243 experienced Python developers were recruited; 202 valid submissions were analyzed. Participants were randomly assigned to Copilot or a control group for a fictional restaurant-review web-server exercise with ten unit tests. Submissions were also reviewed blind, with each reviewed by at least ten people. | The Copilot-access group was reported to be 53.2% more likely to pass all ten tests. Reviewers gave small, statistically significant differences of 3.62% for readability, 2.94% for reliability, 2.47% for maintainability, and 4.16% for conciseness. | Evidence about one product and a defined exercise—not a general estimate of production reliability or long-term maintainability. |
| GitHub and Accenture, enterprise study (2024) | A company-reported study in an enterprise setting, combining a randomized trial, DevOps telemetry, adoption analysis, and surveys. | GitHub reported an 8.69% increase in pull requests per developer and a 15% increase in pull-request merge rate. | Findings from this particular enterprise study; they are not a guarantee of the same effect at other organizations. |
| Fu et al., “Security Weaknesses of Copilot-Generated Code in GitHub Projects” (arXiv version 4, February 6, 2025; accepted for publication in ACM TOSEM in 2025) | Analysis of 733 code snippets from GitHub projects, including snippets attributed to Copilot and two other AI coding tools. | The authors reported security weaknesses in 29.5% of the Python snippets and 24.2% of the JavaScript snippets. With static-analysis warnings, Copilot Chat fixed up to 55.5% of identified security issues in the study setup. | Rates for this analyzed sample, not prevalence estimates for all AI-generated software. The repair result indicates assistance, not complete remediation. |
| Zhang, Zou, Singhal, Sun, and Liu, NIST-listed study of vulnerability repair (2024) | Evaluation of 223 real-world C/C++ code snippets containing memory-corruption vulnerabilities. | Simple, localized memory errors were easier for the evaluated LLMs to repair than complex vulnerabilities requiring broader reasoning across code and program semantics. A comparable general repair rate is not stated (study abstract). | Repair capability depends on the complexity and context of the problem; a success on a local fix does not establish ability to resolve cross-cutting flaws. |
These findings are not contradictory. A tool can help a developer complete a particular exercise or increase activity in a particular organization while generated snippets in another sample contain security weaknesses. The studies examine different outcomes, tasks, and code; neither a passing test suite nor a higher merge rate alone establishes that code is secure or maintainable.
Is AI-generated code secure?
It may be secure, but generation by an AI assistant is not evidence of security. Fu and co-authors examined weaknesses across 43 CWE categories; categories named in the paper’s abstract include insufficiently random values, improper control of code generation, and cross-site scripting. Eight of the categories were among the 2023 CWE Top 25. Those results make security review important, but they describe the paper’s sample and do not establish a universal weakness rate for current assistants, every language, or every codebase.
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The same study found that giving Copilot Chat static-analysis warnings enabled it to fix up to 55.5% of identified issues under the study’s conditions. “Up to” matters: this is not a claim that all findings were fixed, that the remaining code was safe, or that another tool or codebase would get the same result. Any suggested fix still needs independent verification.
Why are some vulnerability repairs harder than others?
The 2024 NIST-listed evaluation by Lan Zhang, Qingtian Zou, Anoop Singhal, Xiaoyan Sun, and Peng Liu tested LLM repair of memory-corruption vulnerabilities in real-world C/C++ snippets. The authors report: “Our findings demonstrate the proficiency of LLMs in rectifying simple memory errors like leaks, where fixes are confined to localized code segments.” They also found that complex vulnerabilities requiring cross-cutting reasoning were more difficult.
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This distinction is practical: a narrowly scoped fix may be visible in one function, while a complex defect may depend on assumptions and interactions across a program. A proposed patch that looks plausible locally can still fail to address the underlying behavior elsewhere. The evaluation supports a conclusion about variation by task complexity, not a claim that models can or cannot repair vulnerabilities in general.
How should teams use generated code?
Use an assistant to propose code, not to certify it. The ordinary engineering checks should remain part of the change process:
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- Check the intended behavior. Confirm that the code addresses the actual requirement and that its assumptions about inputs, dependencies, and surrounding code are valid.
- Run tests that exercise the behavior. Include relevant edge cases and failure paths, not only a happy-path example. Passing tests show that tested cases passed; they do not prove the absence of defects.
- Review the change. Inspect the logic, error handling, data handling, and effects on neighboring code. A reviewer should be able to explain why the change works, rather than accepting plausible-looking output.
- Run security analysis and follow up on findings. If a static analyzer flags an issue, an assistant may help suggest a repair. Rerun the analysis after the change, test the behavior, and verify that the fix addresses the finding rather than masking it.
- Keep release controls in place. Apply the same approval, testing, and release requirements used for other code. Do not treat AI authorship or a tool’s suggestion as a substitute for those controls.
What does NIST recommend for AI-related software development?
NIST SP 800-218A, Secure Software Development Practices for Generative AI and Dual-Use Foundation Models: An SSDF Community Profile, was published July 26, 2024. It adds AI-specific practices, tasks, recommendations, considerations, notes, and references to the Secure Software Development Framework (SSDF) v1.1. It is intended for AI model and system producers and acquirers, providing a lifecycle and governance reference for organizations developing or obtaining AI systems.
The profile is not a certification of an individual generated code fragment. For code-level changes, teams still need to verify behavior, review the implementation, and run their usual security and release checks.
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