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Clear out junk files and repair common Windows errorsFree Scan →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →AI coding assistants can help developers finish some tasks faster, but speed alone says nothing about whether the result is correct, secure, maintainable, or suited to the project. Treat productivity gains as conditional, and verify AI-assisted changes with tests, automated checks, and human review before merging.
Does AI make coding faster?
Sometimes—but the size and distribution of any gain depend on the task, developer, workflow, and how productivity is measured. Studies report positive results in particular settings; they do not establish a universal time saving for every team.
Results from field studies differ by measure
Microsoft Research’s 2025 summary of three randomized field experiments at Microsoft, Accenture, and an anonymous Fortune 100 company reported a 26.08% increase in completed tasks across 4,867 developers (standard error 10.3%). The individual experiments were noisy, so this is evidence about the studied assistant and settings, not a guarantee for another team. The report also found that less experienced developers had higher adoption and greater productivity gains. Microsoft Research’s 2025 summary
A 2025 UK public-sector trial offers a different kind of evidence. From November 2024 to February 2025, 2,500 licences were made available; the main analysis used 424 survey responses from 31 departments, and 73% of respondents reported at least five years of coding experience. Participants estimated an average of 56 minutes saved per working day, including 24 minutes on code creation and analysis. These are survey estimates, not stopwatch measurements. The UK government’s trial report
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The same report found a 15.8% average acceptance rate for suggested code lines, a telemetry measure primarily available for GitHub Copilot. Separately, 39% of surveyed users said they had committed code suggested by an assistant. Neither figure establishes that accepted or committed code was correct, nor does it measure productivity by itself. Survey estimates, telemetry, completed tasks, and elapsed time describe different outcomes and should not be treated as interchangeable.
Benefits are not evenly experienced
An IBM 2025 internal case study of watsonx Code Assistant drew on surveys from two user cohorts (N=669) and unmoderated usability testing (N=15). It found that net productivity increases often occurred, but not for every user. Because this was an internal case study rather than a controlled cross-company benchmark of production defects, it is most useful as evidence that individual experience varies. IBM’s case study
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Does GitHub Copilot improve code quality?
One controlled GitHub study found better results on several measured dimensions for participants with Copilot access, but its scope was narrow. Developers with at least five years’ experience were randomly assigned access to Copilot or no AI for a Python web-server API task. Of valid submissions, 104 were from the Copilot group and 98 from the control group. Functionality was checked with 10 unit tests; readability and other quality dimensions were assessed through blind review. The study was first published in 2024 and updated on February 6, 2025. GitHub’s study
GitHub reported that participants with Copilot access were 53.2% more likely to pass all 10 tests. In code-sample ratings, the study reported differences of 3.62% for readability, 2.94% for reliability, 2.47% for maintainability, and 4.16% for conciseness. These are results from that task and study design, not production defect-rate reductions. The study’s “code errors” in readability reviews did not include functional errors.
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The evidence does not show that all AI-generated code is defective, or that AI-assisted code is uniformly better. Nor does it establish an independent, cross-industry defect rate for AI-assisted code. A controlled task, survey, and field trial answer different questions; none alone predicts how a particular codebase will fare in production.
How do you test AI-generated code?
Use the same engineering checks you would expect for a change written by a person, while paying particular attention to whether the implementation matches the request and project context. GitHub’s documentation guidance is direct: “Always run automated tests and static analysis tools first.” GitHub Docs: AI-generated code review guidance
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- Keep the change focused. Break AI-assisted work into reviewable changes so the intent and diff are understandable.
- Build and test the behavior. Compile or build the project, run existing tests, and add tests for behavior introduced or put at risk by the change.
- Check assumptions and project fit. Compare the implementation with the task, architecture, conventions, and relevant edge cases. A plausible-looking answer is not evidence that its assumptions are right.
- Run the project’s automated analysis. Use linting, static analysis, security and dependency checks, and coverage checks where they are part of the project’s standards.
- Review the diff and dependencies. Examine what changed, including added or altered dependencies, and consider whether the implementation is understandable and maintainable.
- Ask a person to review consequential changes. A reviewer should assess intent, architecture, and risk—not just whether the tests passed.
Tests can encode the wrong expectation or miss behavior that was never exercised. Static analysis catches only the classes of issues it is designed to detect. A passing suite is evidence about the checks that ran, not proof that the change is production-ready.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should teams make verification visible before merge?
Put relevant build, test, and scanning results where reviewers make the merge decision. GitHub status checks can surface those results on a pull request, and protected branches can require selected checks to pass before a merge. GitHub Docs on status checks A required check is useful only to the extent that it runs the right validation; it does not replace reviewing the change’s purpose and risk.
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How should you compare AI-assisted workflows?
Define the outcome before comparing tools or workflows, then keep unlike measures separate. For a team-level evaluation, track more than initial code generation time:
- Throughput or elapsed time: Define whether the measure is completed work, time to completion, or another outcome.
- Correctness: Check meaningful test outcomes, especially coverage of changed behavior.
- Maintainability: Consider readability, complexity, and the effort needed to understand and review the change later.
- Security and dependencies: Use the team’s established scanning and dependency process.
- Total human effort: Include review and correction time, not only time spent generating an initial implementation.
- Who benefits: Record relevant differences in experience and familiarity; adoption and gains can vary between developers.
More accepted suggestions or more lines of generated code do not, on their own, demonstrate higher productivity or better quality. Compare similar tasks and workflows, and report survey estimates, telemetry, test results, and reviewer ratings as distinct measures.
Quick Recap
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