Code Quality Assessment 2026: How to Evaluate Human and AI Code

Code used to come with a fairly obvious author. A developer wrote it. Another developer reviewed it. If something looked odd, someone on the team could usually explain why the decision was made.

That assumption is fading.

AI tools now help developers write, refactor, test, document, and review code. That can increase delivery speed. It also changes where quality problems can hide.

AI-generated code can look convincing at first glance. It may be neatly formatted, consistently named, and able to pass automated checks. The harder problems can sit underneath: logic that is almost right, repeated code, insecure defaults, questionable dependencies, or changes that do not fit the existing architecture.

The real question is whether another capable team can understand the code, verify it, change it safely, and keep building without inheriting avoidable cost and risk.

That is what this guide helps you assess.

What Is a Code Quality Assessment?

A code quality assessment is a structured review of a codebase's internal health.

It answers three practical questions:

• Does the code work reliably?

• Is it safe?

• How expensive will it be to change?

It is not a single score. A grade from one tool is one signal, not a verdict on the whole codebase. It is also not the same as feature testing.

QA testing checks what users see. A code assessment checks what they do not see: structure, clarity, tests, dependencies, and change history.

Martin Fowler describes a useful distinction between external and internal software quality.

Users notice external problems quickly: bugs, slow pages, failed transactions, or confusing screens. Internal quality is less visible. Its effect tends to appear later, in how easily a team can understand, modify, test, and extend the software.

That gives you a practical definition to use with any vendor:

Good-quality code does what the requirements say today and stays affordable to change tomorrow.

That future cost matters because maintainability is one of the less visible factors behind rising software development costs. A feature that should be simple can become expensive when every change touches several fragile parts of the system.

Formal quality models describe the same idea in more detail. ISO/IEC 25010 includes characteristics such as maintainability, reliability, security, and performance efficiency.

A useful assessment does not need to measure every characteristic.

It should focus on the ones that create meaningful risk for the product.

Assess the Code, Not the Author

You usually cannot tell with confidence whether a block of code was written by a person, generated by a model, or produced through both.

In most cases, you do not need to.

Poorly designed code is still poorly designed code.

What authorship changes is the likely failure pattern and the review process needed to catch it.

That distinction matters as AI changes the way software outsourcing teams work. Faster code production does not automatically mean faster verification.

Why Code Quality Matters More With AI in the Loop

Poor code rarely fails all at once. It gets slower and riskier to work with, one change at a time.

The cost hides inside normal delivery. Features take longer. Estimates slip. Bugs return in areas the team thought were fixed.

Research on code quality predates the current AI wave.

A 2022 study by Adam Tornhill and Markus Borg examined 39 proprietary production codebases. Files classified as low quality contained substantially more defects than high-quality files, and issues involving low-quality code took longer to resolve.

The exact numbers should not be treated as universal benchmarks.

The broader lesson is more useful: internal code health affects the cost and difficulty of future work.

AI increases the importance of that lesson in two ways.

First, it can increase code volume.

When teams can produce more code in less time, reviewers may have more changes to understand. More generated code can increase the amount the team must verify and maintain later.

Second, AI can amplify the engineering habits already in place.

Teams with strong testing, review, architecture, and security practices can use AI inside those controls. Teams with weak controls can also produce weak code faster.

Perceived productivity can be misleading as well. A 2025 METR study found that experienced developers in its controlled setting expected AI tools to make them faster, but completed the measured tasks more slowly with AI available.

That was one study, using a specific set of developers, codebases, tasks, and tools. It should not be treated as proof that AI always reduces productivity.

For buyers, the narrower lesson is useful:

Feeling faster is not the same as proving better delivery.

Ask for evidence.

How AI-Assisted Code Can Fail Differently

AI does not create an entirely new definition of bad code.

It can, however, make some patterns more common or harder to notice.

A few deserve more explanation.

Almost-Right Logic

Almost-right logic can be one of the hardest problems to catch.

Obviously broken code often fails immediately.

Code that works in 95% of cases may pass superficial review and still create expensive errors later.

A discount rule might work for normal orders but fail when two promotions overlap.

An access-control check might work for standard users but mishandle an unusual permission combination.

That is why business-critical flows need tests based on requirements, not just tests based on what the implementation currently does.

Duplication Instead of Reuse

Generated code can solve the immediate task without recognizing that similar logic already exists elsewhere.

The result may still work.

The maintenance problem appears later.

If the same pricing rule exists in four places, a future change now has four opportunities to become inconsistent.

One vendor analysis by GitClear reported a large increase in duplicated code in its dataset as AI coding tools became more widely used. Treat that as a signal worth monitoring, not a universal failure rate.

Questionable Dependencies

AI systems can also suggest packages that are unsuitable, outdated, or in some cases do not exist.

That turns dependency checking into more than housekeeping.

This sits alongside the broader risks of outsourcing software work. AI adds another place where weak vendor controls can become your problem later.

What to Assess: Six Areas That Matter

A useful code quality assessment still covers the same fundamental areas. AI does not replace them.

It changes where some of the risk may concentrate.

Two technical terms are especially useful here.

Cyclomatic complexity estimates how many independent paths exist through a piece of code.

More branches usually mean more scenarios to understand and test.

High complexity is not automatically bad. It becomes more concerning when it sits inside code that changes frequently or handles critical business logic.

Coupling describes how strongly one part of a system depends on others.

Imagine changing checkout logic and unexpectedly touching pricing, inventory, shipping, and reporting.

The issue is not that the code is long.

The issue is that one change can create problems in several other places.

Reference Ranges, Not Pass/Fail Grades

Buyers often ask for the one number that means "good code."

There is no universal number.

Published thresholds and tool defaults can still help identify where closer review is needed.

Read these like gauges. They tell you where to investigate.

For AI-heavy codebases, trends across releases can be more useful than a single snapshot.

Automated Tools vs. Human Review

: Can automated tools assess code quality?

: Partly.

Traditional scanners are fast and consistent at finding repeatable patterns.

AI review assistants can add summaries, explanations, and another pass over the code.

Neither can take responsibility for whether the software meets the business need or whether its design makes sense for the system.

Static analysis means examining code without running it.

SAST, or static application security testing, applies a similar idea to security weaknesses.

SCA examines third-party software components.

All of these sit inside broader secure development practices. A clean scanner report does not prove that the entire system is secure.

Automation still struggles to judge:

• Whether the code implements the business rule correctly

• Whether each module has a clear responsibility

• Whether the tests check behavior that actually matters

• Whether a simpler design would have worked

• Whether the architecture fits where the product is going

One misconception deserves a direct answer:

AI-reviewed code has not automatically been verified.

It has been reviewed by another probabilistic system that can still miss logic, context, architecture, and security problems.

AI review can be useful.

It should be treated as an additional signal, not final approval.

For high-stakes acceptance, takeovers, or regulated software, independent software testing can add another layer of verification.

Where Code Quality Numbers Mislead

Most weak assessments are not completely wrong.

They are incomplete in predictable ways.

AI can make some of those gaps more important.

Clean Formatting, Weak Logic

AI-generated code often looks tidy.

That makes formatting and style less useful as evidence of deeper quality.

Neat naming does not prove that the business rule is right.

Tests That Agree With the Code

Tests created from the implementation can end up confirming whatever the implementation already does.

If the code contains a wrong assumption, the test may repeat the same assumption.

Coverage can therefore look strong while important behavior remains unverified.

Two useful additions are:

Branch coverage, which checks whether different decision paths were exercised.

Mutation testing, which deliberately changes the code to see whether the test suite detects the change.

If meaningful mutations survive, the tests may not be protecting the behavior as well as the coverage percentage suggests.

Green Dashboards, Fragile Products

A team can show high coverage, clean scans, and fast reviews while still producing unstable software.

This reflects Goodhart’s law: when a measure becomes a target, teams can improve the number without improving the underlying outcome.

In software, that can mean writing tests mainly to raise coverage or suppressing warnings rather than fixing the underlying issue.

Pair input metrics with outcomes.

Look at:

• Production defects

• Incidents

• Escaped vulnerabilities

• Rework

• How often recent code must be rewritten

• How long common changes take

Averages That Hide the Hotspot

A codebase can look healthy on average while one critical area creates most of the risk.

Problems become especially important when code is both difficult to understand and changed frequently.

Adam Tornhill's hotspot approach combines change frequency with measures of complexity or code health to help identify those areas.

If review time is limited, hotspots are often a better place to spend it than treating every file equally.

How Deep Should the Assessment Go?

The right depth depends on what the software does. A spelling checker and a payment system do not need the same scrutiny.

The goal is not to buy the deepest audit every time. It is to match scrutiny to risk.

An early prototype does not need the same review as a payments platform.

A product built largely through AI tools by people without software engineering oversight deserves special attention because there may be no experienced person who has reviewed the system as a whole.

For fintech products, security, data handling, and compliance can make independent review much more important than it would be for a low-risk internal tool.

Evaluating AI-Assisted Code From Outsourcing Partners

Most development vendors now use AI tools in some form.

The useful question is not simply whether they use AI.

It is whether their engineering process catches what AI can get wrong.

Before You Choose a Vendor

Portfolio code proves less than it appears to.

You may not know who wrote it, how much AI assistance was involved, how much was rewritten, or whether the developers assigned to your project were involved.

A stronger test is a small paid exercise:

1. Give the team a contained task using a realistic codebase or problem.

2. Ask the developers who would actually work on your project to submit the change as a pull request.

3. Have them walk you through their decisions.

4. Change one requirement and ask how they would adapt the implementation.

The walkthrough is what makes the exercise useful.

Developers who understand the code can usually explain the tradeoffs behind it, regardless of whether AI helped draft part of the solution.

This can form part of shortlisting a development partner.

Ask each candidate:

• Which AI tools do you use?

• Which tasks are they used for?

• What human review does every AI-assisted change receive?

• Who approves the final change?

• How do you verify new dependencies?

• Which security and quality checks can block a merge?

• Which reports will we receive?

• How often will we receive them?

At Handover or Vendor Takeover

Assess the code before final payment or before accepting responsibility for the system.

After handover, your ability to require corrective work may be lower.

A maintainability or architecture review can expose work that should be completed before another team takes ownership.

At handover, request:

• Full source code

• Build and deployment instructions tested by someone outside the original implementation team

• Current quality and security reports

• Important scanner rule settings

• A software bill of materials, or SBOM

• Relevant dependency and license information

• Architecture documentation

• Known issues and technical-debt notes

Also verify that important dependencies were deliberately selected and are still maintained.

Code that only the previous team, or the previous team's prompts, can explain creates a lock-in risk.

That becomes especially important if you intend to move ongoing maintenance and support to another provider.

During the Engagement

Do not wait until handover to discover whether quality is deteriorating.

Review the same important signals at each milestone.

Watch trends in:

• Duplication

• Complexity

• Vulnerabilities

• Defects

• Rework

• Test quality

• Review coverage

Responsibility also depends on the engagement model.

The distinction between staff augmentation and project-based outsourcing matters here.

In a project-based contract, the vendor usually carries more responsibility for the delivered outcome.

With staff augmentation, your own technical leadership generally controls more of the engineering process.

That changes who defines, checks, and enforces the quality standards.

Contract Terms for AI-Assisted Work

AI-related expectations should be specific enough to verify.

Useful areas to cover include:

• Disclosure: which AI tools are used and for what work

• Accountability: a named engineer approves important changes

• Critical paths: require human review of AI-assisted changes involving payments, authentication, permissions, or sensitive data

• Dependencies: new packages must be vetted and recorded

• Acceptance: agreed checks must pass at each milestone

• Documentation: significant architecture and implementation decisions remain explainable without relying on old prompts

Avoid one numeric quality target.

A single number is easier to optimize than a group of meaningful controls.

Ownership, licensing, privacy, and other legal questions around AI-generated material can vary by jurisdiction and contract.

Have legal counsel draft the enforceable wording.

When outsourcing development to teams that use AI, clear controls turn "we use AI responsibly" into something you can actually examine.

What a Useful Code Quality Assessment Report Includes

A useful report should help you decide what to do.

Look for:

• Scope: what was assessed and what was excluded

• Methods and tools: including important rule settings

• Findings: ranked by severity and tied to specific parts of the system

• Hotspots: not just codebase-wide averages

• Security and dependency findings: including relevant license risks

• Architecture observations: especially around coupling and changeability

• Business impact: explained in plain language

• Recommended actions: with priorities and rough effort where possible

If a report cannot tell you what to do next, it is closer to a scan printout than a full assessment.

The Bottom Line

AI changes how code gets produced, not what good code needs to do.

A useful assessment should show where the risk is, what it means, and whether another capable team can safely understand, maintain, and extend the software.

The goal is not a perfect score. It is confidence in the code you are taking responsibility for.