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AI coding assistants can improve selected measures of code quality, but they do not make generated code reliably secure. A controlled GitHub study found that experienced Python developers with Copilot were more likely to pass a defined set of tests and received modestly better expert ratings for readability, reliability, maintainability and conciseness. Other studies found security weaknesses in substantial portions of sampled assistant-generated snippets, and a 2025 benchmark found that models were generally better at detecting vulnerable code than at generating secure code or repairing every vulnerability.
The practical conclusion for open-source maintainers is conditional: an assistant can accelerate drafting and help with some quality tasks, but every suggestion remains untrusted code. Security depends on the language, task, model, repository context and the human testing and review process around it.
What “quality” and “security” mean in the available evidence
Code quality is not one measurement. The strongest controlled evidence separated several outcomes instead of declaring that AI code was simply “better.” The GitHub study measured whether code passed unit tests, readability errors found by reviewers, expert ratings for reliability and maintainability, conciseness, and whether reviewers approved a submission. Passing tests and readable code are useful signals, but neither proves that an implementation is secure.
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Security is a separate property. A function can satisfy visible tests and follow local style while still using predictable randomness, mishandling generated code, exposing a cross-site scripting path or creating another weakness that tests do not exercise. Security review therefore cannot be replaced by a quality score, a successful build or an assistant’s confidence.
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| Dimension | What it can tell a maintainer | What it cannot establish by itself |
|---|---|---|
| Unit-test results | Whether the implementation met the tested behaviors in that exercise | Absence of vulnerabilities, unsafe dependencies or untested edge cases |
| Readability and maintainability ratings | Whether reviewers considered the code understandable and easier to maintain | Correct security boundaries or safe handling of hostile input |
| Reliability ratings | Expert judgment about dependable behavior in the task studied | Security under adversarial use or production-scale operation |
| Static-analysis findings | Potentially dangerous patterns identified by a scanner | That every vulnerability has been found or fixed |
| Vulnerability detection scores | How well a model recognizes weaknesses in supplied code | That the same model will generate secure code from a blank prompt |
What the major studies actually found
GitHub’s controlled Python exercise
GitHub reported a randomized study in an article published November 18, 2024 and updated February 6, 2025. The researchers recruited 243 developers who each had at least five years of Python experience. Of those, 202 submitted valid solutions: 104 had access to Copilot and 98 did not. Participants implemented a web server for fictional restaurant reviews and were evaluated against ten unit tests.
GitHub reported that developers with Copilot had a 53.2% greater likelihood of passing all ten tests than the control group. That is a result for this bounded exercise, not a measured reduction in defects across real open-source repositories.
A second phase asked 25 developers to review submissions without knowing whether an assistant had been used. The submissions received 1,293 reviews in total. GitHub reported fewer readability errors in the Copilot group and relative improvements in expert ratings of 3.62% for readability, 2.94% for reliability, 2.47% for maintainability and 4.16% for conciseness. The study’s definition of a code error focused on readability and coding-practice issues; it did not count functional failures as that error measure.
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Fu et al.: weaknesses in sampled GitHub-project snippets
Fu and colleagues analyzed 733 snippets from GitHub projects generated with Copilot, CodeWhisperer and Codeium. Their February 6, 2025 arXiv version (accepted for ACM TOSEM 2025) reported identified security weaknesses in 29.5% of sampled Python snippets and 24.2% of sampled JavaScript snippets. The analysis covered 43 CWE categories, including eight categories appearing in the 2023 CWE Top 25.
Reported categories included insufficiently random values, improper control of code generation and cross-site scripting. These are findings in the selected project snippets and tools, not a prevalence rate for all AI-generated code or all open-source projects. Sampling choices, language coverage and assistant versions limit how broadly the percentages can be applied.
The same study tested a remediation workflow. When Copilot Chat received static-analysis warning messages, it fixed up to 55.5% of the identified issues under the study conditions. “Up to” matters: the result is not a guarantee that warning messages will produce a safe patch, and it does not show that the remaining issues are harmless.
Professionals use assistants even for security-sensitive work
Klemmer and colleagues combined 27 semi-structured interviews with software professionals and an analysis of 190 relevant Reddit posts and comments. The May 10, 2024 qualitative study found that participants used general-purpose and coding-specific assistants for activities such as code generation, threat modeling and vulnerability detection despite concerns about security and quality.
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Participants’ mistrust led them to check suggestions in much the same way they would check code written by another person. The authors recommend critical checking. Because this was a qualitative sample, it describes practices and concerns rather than estimating how common they are among all developers.
The 2025 multi-task security benchmark
Mou and colleagues evaluated 20 proprietary and open-source large language models in the ACL 2025 paper, published in July 2025. The benchmark covered code completion, vulnerability repair, vulnerability detection and vulnerability classification.
Its broad result was an important asymmetry: most models were better at identifying vulnerable code than at generating secure code, and they struggled with particular vulnerability types and with repairing every weakness. Detection skill should therefore not be treated as evidence that a model’s new code is secure. The benchmark is comparative model evidence, not a direct estimate of vulnerability incidence in production open-source software.
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Developer confidence is not the same as measured safety
In an October 10, 2023 blog post, GitHub reported that 85% of surveyed developers felt more confident in their code quality when using Copilot and Copilot Chat, and that code reviews were completed 15% faster. These are vendor-published survey and workflow findings. Confidence and review speed can be useful operational outcomes, but they do not demonstrate that vulnerabilities fell or that faster reviews were equally thorough.
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Why assistants can improve quality while still creating security risk
Tests cover intended behavior, not every attacker behavior
An assistant often receives a prompt, surrounding code and visible tests. It can produce an implementation that satisfies those tests while omitting authorization checks, mishandling malformed input or choosing an unsafe API. Unless the test suite exercises the security boundary, a passing result leaves that boundary unexamined.
Readable code can make a mistake easier to miss
Concise, idiomatic code is easier to review, but readability does not change the underlying threat model. A neatly formatted query construction can still permit injection; a clear web response can still reflect unescaped input. Reviewers must ask what data is trusted, where it crosses a boundary and which assumptions an attacker can violate.
Generated fixes may address symptoms
Providing a scanner warning can help an assistant propose a patch, as the Fu study demonstrated in some cases. A patch may nevertheless silence a warning without preserving authorization logic, introduce a different weakness or fail on an untested path. The scanner result, the diff and the surrounding design all require human verification.
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Assistants may not know deployment settings, secret-management rules, downstream consumers, threat models or undocumented compatibility constraints. Open-source repositories also evolve through contributions from many people. A locally plausible suggestion can conflict with a project’s security policy or create a maintenance burden for later contributors.
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How AI assistance changes the open-source contribution workflow
Generation and completion
Assistants can reduce the time needed to draft repetitive code, tests, documentation and adapters. The benefit is greatest when the contributor can state the interface and verify the result. For security-sensitive routines—authentication, authorization, cryptography, parsing, sandboxing, payment logic or code that executes generated content—speed should not lower the review threshold.
Review and integration
Because assistant output can look polished, maintainers should review the actual diff rather than infer quality from its style or from the fact that a contributor used a familiar tool. Ask what changed, which assumptions are new, and whether the patch alters trust boundaries, permissions, data flow or dependency behavior.
Maintenance and future patches
Open-source quality includes the cost of understanding and safely changing code later. A concise generated function may help today but leave unusual abstractions, duplicated logic or undocumented decisions. Require comments and tests where the security rationale would otherwise be lost, and avoid accepting generated complexity merely because it works in the first version.
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- Identify assistant-generated or assistant-influenced sections. This is not a reason to reject a contribution; it tells reviewers where to examine assumptions and generated patterns closely.
- Read the diff line by line. Trace inputs from entry points to storage, output, network calls and process execution. Check authentication, authorization, validation, encoding, error handling and logging.
- Run the project’s complete test suite. Confirm that existing tests pass, then add tests for boundary conditions and abuse cases introduced by the change. A passing suite is evidence about tested behavior, not a security certificate.
- Run language-appropriate static analysis and security scanning. Treat findings as leads to investigate. If an assistant proposes a fix, verify the corrected data flow and test the vulnerable path rather than accepting the patch because the warning disappeared.
- Review dependencies and configuration. Check new packages, version constraints, permissions, defaults, secrets handling and deployment settings. Generated code can create risk outside the edited file.
- Ask for a security explanation. The contributor should be able to explain the threat model, trusted inputs, rejected inputs and why the chosen API is appropriate. If the explanation is missing, request changes before merge.
- Record decisions that future maintainers need. Document non-obvious security assumptions, limitations of tests and any accepted residual risk in the project’s normal issue or review record.
How to compare assistants without overclaiming
There is no evidence here for a universal product ranking. Compare an assistant only on the task your project actually performs and keep generation, detection and repair as separate capabilities.
| Comparison question | Why it matters |
|---|---|
| Which language and framework were evaluated? | Results from Python or JavaScript snippets cannot automatically be transferred to another stack. |
| Was the task completion, detection or repair? | A model may identify a weakness yet generate an insecure first draft or an incomplete fix. |
| What was the study population and sample? | A controlled exercise, sampled repository snippets, interviews and a model benchmark answer different questions. |
| Were reviewers blind to assistant use? | Blinding helps separate code judgments from expectations about the tool. |
| What controls surrounded the tool? | Tests, static analysis, security review and dependency checks determine how an output is governed. |
| What happens when the model is wrong? | Measure how quickly reviewers detect, explain and safely repair a bad suggestion, not only how fast code is produced. |
Questions maintainers should ask before approving AI-assisted code
- What security property is this change supposed to preserve or add?
- Which inputs are attacker-controlled, and where are they validated and encoded?
- Does the patch change permissions, identity, secrets, generated-code handling or external data flow?
- Which tests would fail if the security assumption were broken?
- Did a scanner identify a warning, and was the underlying cause fixed rather than hidden?
- Can another maintainer understand the design without relying on the assistant’s explanation?
- Are new dependencies, licenses, versions and configuration changes acceptable for this repository?
Bottom line
AI coding assistants are productivity tools, not security authorities. Controlled evidence supports modest quality gains in a specific Python exercise, while snippet analysis and a broad benchmark show that insecure generation remains a material problem. Open-source projects gain the most when assistants accelerate drafts and reviewers apply the same—or higher—standards of testing, static analysis, dependency review and security reasoning to every proposed change.
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