Artificial intelligence is accelerating code generation. However, making software work is not the same as making it reliable, secure, and sustainable. As development gets faster, who will take responsibility for quality?
Imagine that you want to build an application. You describe your requirements to an AI-powered coding tool in a few sentences:
“Create a modern login screen with email and password fields. Add a forgotten password link and make the design responsive for mobile devices.”
Within a short time, you have a working interface. With a few additional prompts, the AI adds a registration flow, database integration, and error messages.
Completing in minutes or hours what might previously have taken days is undoubtedly impressive. However, it raises an important question:
Does working software automatically mean quality software?
This is where the discussion about vibe coding and software quality begins.
What Is Vibe Coding?
Vibe coding is a development approach in which a person describes what they want in natural language and progresses by using code generated by artificial intelligence, rather than manually writing and controlling every line of code.
In a typical vibe coding workflow, the developer may:
- Explain technical requirements in natural language.
- Apply AI-generated code to the project.
- Send error messages back to the AI tool.
- Refine prompts until the desired result appears.
- Focus more on the visible output than on every technical detail behind it.
Using artificial intelligence for code suggestions, code reviews, or debugging support does not automatically qualify as vibe coding.
Vibe coding generally refers to a workflow in which a significant part of the development process is guided through natural language prompts, while the internal details of the generated code may receive less attention.
This approach can be highly valuable for rapid prototyping, MVP development, and idea validation.
However, making code easier to produce does not automatically make software quality easier to achieve.
Why Has Vibe Coding Become So Popular?
Vibe coding significantly lowers the barrier to software development.
Ideas that once required extensive technical knowledge, time, or a large development team can now be converted into working prototypes much more quickly.
This approach can be particularly useful for:
- Rapid idea validation
- MVP and prototype development
- Creating small internal tools
- Testing different interface alternatives
- Accelerating repetitive development tasks
- Helping people with limited technical knowledge turn ideas into tangible products
The value of vibe coding is not limited to generating code faster. It can also help developers focus on the problem they are trying to solve instead of spending all their time on syntax and implementation details.
However, speed introduces new risks.
When more code is produced in less time, the amount of code that must be reviewed, understood, and tested also increases.
Is It Enough for the Application to Work?
At first glance, an application created through vibe coding may appear to work perfectly.
Users may be able to log in, navigate between pages, click buttons, and view data. However, successful completion of basic user flows does not mean the application is ready for production.
For example, an AI-generated login screen may leave the following questions unanswered:
- What happens after multiple incorrect password attempts?
- Is there any protection against brute-force attacks?
- How long does a user session remain active?
- Do error messages reveal unnecessary information?
- Are passwords stored securely?
- Can the form be completed using only a keyboard?
- Can screen readers identify the form fields correctly?
- What happens when the internet connection is lost?
- Does the layout break on different screen sizes?
- Is the same action triggered twice after a double-click?
- How does the interface behave when the server responds slowly?
Unless these details are included in the prompt, an AI tool may only implement the most visible and expected path.
The resulting product may technically work. However, it may still contain serious security, accessibility, performance, or usability problems.
An application opening successfully does not mean it is ready for production.
The Hidden Cost: Technical Debt
The main objective of vibe coding is often to reach a working result as quickly as possible.
As a result, architecture, readability, and long-term maintainability may receive less attention.
Problems that initially appear minor can later turn into significant technical debt:
- Duplicated code performing the same function
- Unused or outdated dependencies
- Inconsistent architectural patterns
- Complex functions that are difficult to modify
- API keys and configuration values hardcoded into the source code
- Incomplete error handling
- Insufficient logging
- Missing security controls
- Components that are difficult to test
- Unclear naming conventions
- Missing technical documentation
These issues may not appear critical during the prototype stage. However, as new features are added and more developers join the project, the codebase can become increasingly difficult to manage.
A codebase that nobody on the team fully understands represents a serious sustainability risk, even when the application is currently working.
Artificial intelligence does not take responsibility for the code it generates.
That responsibility belongs to the people who add, approve, and release the code.
Is It Enough to Ask AI to Test AI-Generated Code?
Artificial intelligence can be used not only to write code but also to generate test cases and automation scripts.
Creating tests from user stories, drafting unit tests, generating test data, and summarizing bug reports are all areas in which AI tools can provide significant value.
However, validating AI-generated code only with another AI-generated output is not sufficient.
The model generating the code and the model generating the tests may:
- Miss the same requirement gap.
- Rely on the same incorrect assumption.
- Focus only on successful user journeys.
- Fail to represent real user behaviour.
- Accept a result that is technically correct but wrong from a business perspective.
- Share similar blind spots because they rely on comparable patterns and data.
For example, if the requirement does not clearly state that a payment must only be processed once, the AI may create a button that allows duplicate transactions. The AI-generated tests may also fail to check repeated clicks.
Artificial intelligence should therefore not be treated as an authority that guarantees correctness.
It should be treated as a tool that increases the capacity of development and quality teams.
The number of generated tests is less important than whether those tests cover meaningful risks.
Who Is Responsible for Quality?
Quality after vibe coding should not be considered the sole responsibility of software testers.
Quality is not a final approval step performed by one person at the end of development. It is a shared responsibility across product, development, testing, security, and operations teams.
Product teams
Product teams must explain not only what should be developed but also what correct behaviour looks like.
Ambiguous requirements create risks for AI tools just as they do for human developers.
A quality result cannot be expected without clear acceptance criteria, business rules, error scenarios, and user expectations.
The instruction “Create a payment screen” is not enough.
The team must also define what happens when a payment fails, whether the user can retry, how duplicate payments are prevented, and which messages are displayed.
Software developers
The developer’s role may shift from writing every line manually to guiding, reviewing, and validating generated code.
However, technical responsibility does not disappear.
A developer should understand:
- What the generated code does
- Which data it processes
- Which dependencies it uses
- Which security risks it introduces
- How it fits into the existing architecture
- How it will be tested
- How it can be rolled back when necessary
Code that is not understood should not be moved into production.
Even when artificial intelligence generates the code, a human still approves the pull request and makes the merge decision.
Testing and quality teams
Vibe coding does not eliminate the need for testers. Instead, it makes their analysis and risk assessment skills even more valuable.
Testing and quality teams:
- Question ambiguous requirements.
- Identify assumptions made by artificial intelligence.
- Define positive and negative scenarios.
- Evaluate boundary conditions.
- Simulate real user behaviour.
- Build risk-based testing strategies.
- Validate AI-generated tests.
- Use exploratory testing to uncover unexpected problems.
- Make quality risks visible to the wider team.
A tester is therefore not simply someone who finds defects.
A tester is also someone who discovers unknown risks, asks the right questions, and guides the team towards better quality decisions.
Security teams
Security should not be added as a final step in applications built through vibe coding.
AI-generated code may introduce problems such as:
- Hardcoded API keys
- Unvalidated user inputs
- Weak authentication controls
- Overprivileged service accounts
- Outdated or vulnerable dependencies
- Missing access controls
- Sensitive data written into logs
Static code analysis, dependency scanning, secret scanning, and security testing should be integrated into the development workflow.
Rapid development should never become an excuse for rapidly skipping security controls.
A Five-Layer Quality Approach for Vibe Coding
Rejecting vibe coding completely is neither realistic nor necessary.
A more effective approach is to use it with the right quality controls.
1. Clarify the requirements
Before generating code, define user needs, business rules, and acceptance criteria.
Do not describe only the expected successful outcome. Include error situations, exceptional behaviour, and boundary conditions.
2. Review the generated code
Do not accept AI-generated changes without review.
Examine the architecture, data flow, dependencies, error handling, and security controls.
Ask the tool to explain unclear sections, simplify complex logic, or rewrite problematic code.
3. Use multiple testing levels
Checking only whether the user interface works is not enough.
Combine unit, API, integration, system, and user acceptance testing.
Protect critical user flows with automated tests, while ensuring that every test validates a meaningful risk.
4. Apply exploratory testing
Predefined test cases generally validate expected behaviour.
Real users do not always interact with software in expected ways.
Missing data, rapid clicks, interrupted connections, unusual operation sequences, different devices, and unexpected user habits should be evaluated through exploratory testing.
5. Monitor production
Passing every test does not guarantee that the product will operate without problems in the real world.
Monitor application logs, error rates, performance metrics, critical user journeys, and user feedback.
The team should also have a safe release and rollback strategy for critical failures.
Will the Role of Software Testers Decrease?
Faster code generation does not mean less testing is required.
On the contrary, when more code, features, and changes are produced in less time, the area that must be validated expands.
The role of software testers will transform rather than disappear.
Some repetitive checks can be accelerated with artificial intelligence and test automation. However, human expertise becomes even more valuable in areas such as:
- Interpreting risk
- Questioning ambiguous requirements
- Understanding business processes
- Analysing user behaviour
- Discovering unexpected scenarios
- Evaluating security and ethical impact
- Validating AI-generated outputs
- Guiding teams on quality decisions
Artificial intelligence can generate hundreds of test cases.
However, determining which risk is critical to the business, which defect could damage user trust, or which behaviour conflicts with business goals still requires human judgement.
Speed and Quality Are Not Opposites
Vibe coding demonstrates that the relationship between people and software development is changing.
Developers may write less syntax while taking on more responsibility for guidance, validation, and decision-making.
The main problem is not that artificial intelligence writes code.
The real problem begins when AI-generated code is used without being understood, questioned, reviewed, or tested.
When applied responsibly, vibe coding can:
- Bring ideas to life more quickly
- Improve team productivity
- Accelerate prototyping
- Make software development more accessible
Without proper controls, it can also create technical debt, security vulnerabilities, performance problems, and unsustainable systems.
The successful teams of the future will not necessarily be the teams that generate the most code with artificial intelligence.
They will be the teams that combine the speed of artificial intelligence with human experience, strong testing practices, effective security controls, and a shared quality culture.
Artificial intelligence may write the code.
But protecting quality is still our responsibility.