AI Writes Code, But Who Maintains It? The Hidden Challenges of AI-Generated Software

Artificial intelligence is changing how software is written. AI-powered tools like GitHub Copilot, OpenAI’s Codex, and Amazon CodeWhisperer can generate code snippets in a matter of seconds, helping developers automate monotonous work and speed up software development. 

Rather than spending long hours writing boilerplate code, developers can now use AI to assist them with anything, from simple functions to complex algorithms. If all of this sounds like a breathtaking change to you, it probably is. 

The software can be released by companies in a much shorter time frame while keeping development costs to a minimum and allowing engineers to focus on higher-level problem-solving. The reality, however, is a much more intricate beast. 

AI-generated code brings along the unseen complication of long-term maintainability.

Coding is merely the first step in software development. Software has to be updated, optimized, and secured over time in order to remain operational. 

The evolution of AI in software development raises critical questions about long-term maintainability. Understanding how AI-driven solutions impact data integrity and decision-making is crucial in today’s tech landscape.

Human developers have a thorough understanding of the business logic embedded in their applications, follow best practices, and ensure that their code is structured for easy modification. AI, however, cannot reflect the long-term maintenance of software.

This brings forth some serious questions:

• How will developers maintain AI-generated code without its corresponding context and business logic?
• What if AI produces complex code that has become unreadable?
• How will companies ensure that the AI-generated software is secure and follows the rules of the industry?

Without any framework to safeguard this, businesses may be left with a high level of technical debt, security vulnerabilities, and compliance issues to deal with later – usually at an exorbitant price. 

AI may be able to write codes, but it will always take the hands of human developers to maintain and improve those codes. 

We’ll take a careful look at why, in terms of maintainability, AI-generated code is generally deficient and what the implications of that are for the way ahead in programming.

How Is Code Maintained? 

Writing code isn't the end of the story. Keeping code running smoothly means constant care to keep things secure and working well as time goes on. This means fixing problems, making things faster, adding new features, and keeping up with the changes we need to make.

1. Finding and Fixing Bugs

Almost no program works right the first time. Coders keep testing and fixing code to find and fix mistakes. These can be minor annoyances or serious security holes that mess up the whole system. Code made by AI might have hidden issues that a person needs to fix.

2. Code Refactoring

Code gets messed up and also slows down the line of time. Refactoring code involves restructuring existing code to make it more readable and less repetitive, while preserving its functionality, to simplify future updates or extensions. Much AI code needs to have this when it becomes too complex or contradictory.

3. Optimizing Code Performance

As user load increases, the program should handle it smoothly. To achieve this, coders should design the code to scale efficiently. 

AI-made code may run fine at a certain moment, but someone may need to make sure that somebody has done tweaks to keep it running well under increased usage.

4. Security Enhancements

The hazards online never die; thus, the old code can be insecure. The updates usually are there to patch those holes. AI code can sometimes face security vulnerabilities, so attentive programmers should closely monitor it and ensure it aligns with the latest security standards.

5. New Legislation and Features

Companies must update their software with all the newest features and make sure it complies with all the newest legislations. AI-written code might not always be able to cover these bases, so programmers need to double-check it and adjust it for what is needed.

Why AI-Generated Code Lacks Maintainability

While AI-generated code can be useful, it often introduces several challenges that make long-term maintenance difficult. These issues stem from AI’s fundamental limitations – it doesn’t understand business requirements, it generates inconsistent code, and it can introduce serious security risks.

1. Insufficient Contextual Knowledge

AI models generate code based on patterns they have found in massive databases.   Despite being taught billions of lines of code from open-source sources, they don't truly "understand" the code's intent. 

This results in a serious issue: AI systems cannot comprehend project-specific requirements, business logic, or industry regulations.

For example, if an AI-powered coding assistant constructs a function to handle financial transactions, it may produce syntactically correct code that appears to work.  However, the AI: 

• Lacks knowledge of the organization's internal security procedures; it may not appropriately encrypt data.
• Won't always adhere to PCI DSS and other industry compliance standards (for payment security).
• May overlook recommended techniques for addressing errors that a human developer would inevitably incorporate.

Because of this, developers must carefully review and refine AI-generated code to ensure it aligns with business needs. While it approaches human creativity, AI lacks the ability to solve problems in an innovative way as humans can. If they don’t, the software may contain hidden flaws that become costly to fix later.

2. Code Complexity and Readability Issues

AI-generated code may work, but it's not necessarily easy to comprehend, modify, or expand upon. Code maintainability depends on clarity and structure since these allow future developers to understand how the code works and make the appropriate changes.  This is where AI usually falls short. This is the cause:

• Complicated logic: Code produced by AI is frequently more complex than is necessary. AI may add needless loops, conditions, or redundant operations in place of designing clear, well-structured functions.
• Inconsistent coding styles: AI models, particularly those trained on a variety of sources, don't always adhere to a single coding style. Teams may find it more difficult to work together if this results in inconsistent work within the same project.
• Redundant code: AI may produce lines of code that are superfluous or repetitive. This slows down future development over time by adding to technological debt.

Imagine a software development team that uses AI to create web application backend logic. The team will need to invest more time in refactoring the AI's code before proceeding if it generates inconsistent or excessively complex code. 

The efficiency improvements that AI was meant to bring about are diminished by this extra work. 

3. Security Vulnerabilities

One of the main concerns with AI-generated programming is security. AI models may inherit antiquated or unsafe coding techniques since they are trained on publicly accessible code sources. Typical security flaws in AI-generated programming include the following:

• Hardcoded credentials: AI has the potential to produce code that contains private data, such as database passwords or API keys, leaving apps vulnerable to security breaches.
• SQL injection vulnerabilities: Applications are susceptible to SQL injection attacks when AI tools create database queries without the appropriate parameterization.
• Inadequate input validation: AI-generated functions might not adequately check user input, which could lead to exploits, such as buffer overflow attacks and cross-site scripting (XSS).

Just as developers weigh the pros and cons of Surfshark vs NordVPN to ensure secure online access, they must also carefully evaluate AI-generated code for potential security vulnerabilities before deployment.

For companies handling sensitive data – such as healthcare, finance, or government organizations – these risks can have serious consequences. AI-generated code should always be reviewed by security experts before being implemented in production systems.

The Real-World Impact of Unmaintainable AI Code

Are you using AI code-writing features? Here is a compelling example that will make you consider the threats such code might bring to your AI Software Development. 

Picture a company, a startup, developing a client management system essentially powered by AI coding software. 

Everything seems pretty smooth at the beginning; the AI is doing a terrific job producing the backend logic, API endpoints, and the necessary authentication flows in record time. Things operate well… Until problems start emerging: 

• New participants cannot carry out any type of integration because of the disparity in how the AI-powered codebase operates. 
• Security audits bring this shocking news to the company:  they found several pitfalls in the authentication system that needed urgent remedial fixes. 
• Performance is dropping slowly – redundant, ineffective code blocks are tripping up the machinery. 

Then comes the realization that AI-generated code bears striking similarities to copyrighted open-source projects. The company is facing rising costs from all the extra modifying and patching of solutions required to avoid legal risks. The supposed time saver is, instead, a time bomb ready to explode.

How Developers Can Mitigate the Risks of AI-Generated Code

AI-generated code accelerates development, but maintaining its reliability requires the right approach. Learn how AI Software Development Optimizing Custom Instructions enhances software quality and efficiency.

Despite these challenges, AI-generated code isn’t entirely bad – it just needs proper oversight. Here’s how developers and businesses can make AI-assisted coding more maintainable:

• Implement strict code reviews: AI-generated code should always be reviewed by human developers before it’s merged into a project. This helps catch security flaws, inefficiencies, and inconsistencies.
• Follow standardized coding practices: Teams should enforce consistent coding styles and best practices to ensure AI-generated code aligns with human-written code.
• Use AI as an assistant, not a replacement: AI coding tools should complement human developers, not replace them. Developers should treat AI-generated code as a starting point that requires refinement.
• Prioritize security testing: Businesses should run regular security audits on AI-generated code to prevent vulnerabilities from slipping into production.

By taking these steps, companies can harness the power of AI while ensuring that the software remains maintainable, secure, and legally compliant.

Final Thoughts

Although AI can speed up development, it is unable to consider how to keep code secure, easy to maintain, and compliant with regulations. 

Businesses that rely too much on AI to do their coding without monitoring the situation with human coders may eventually have untidy code, security flaws, and maintenance problems.

Using AI-generated code isn’t the only answer.  Businesses must implement robust human review processes, security audits, and coding best practices to ensure AI-assisted development remains secure and reliable.

Although AI can write code, humans will always be required to maintain it.