Artificial Intelligence (AI) is rapidly transforming the Software Development Lifecycle (SDLC), bringing new levels of efficiency and productivity. The impact of AI is as transformative as the industrial revolution, with dramatic productivity gains, the disappearance of some old jobs, and the emergence of new roles as the rules of the world change.
While AI holds tremendous promise, it’s crucial to separate the hype from reality. Much of the conversation focuses on greenfield projects, but most developers work with large, complex legacy codebases, where even small mistakes can have significant consequences. As AI tools reshape how software is built, the real question isn’t whether they work – it’s whether we’re sacrificing long-term value for short-term speed.
AI’s Expanding Role in Development
AI is making remarkable progress across various stages of the SDLC. Developers are not just co-authoring code with AI, but increasingly, entire features are being generated by coding agents. As this shift continues, the need to ensure that AI-generated code meets high quality and security standards becomes more important. Enforcing these standards early in the development process, before code reaches the main branch and production, is vital for rapid adoption of AI tooling.
Companies are adopting AI code generation not only through coding assistants in IDEs but also through autonomous agents that suggest code in the form of pull requests. Interestingly, the 2024 DORA report highlights that a 25% increase in AI adoption correlates with a 7.2% decrease in delivery stability. This means that as AI takes on a larger role in development, it’s more crucial than ever to implement guardrails at multiple levels to ensure that the speed of code generation doesn’t compromise code stability, security, or performance.
The assurance of quality and security in AI-coauthored code is becoming increasingly important, enabling teams to apply specific quality gates. However, with this capability comes the need for clear accountability. Developers must remain fully responsible for any code they accept, no matter its source. If AI-generated code causes issues, such as bugs, security flaws, or maintenance challenges, it’s the developer’s responsibility to address them.
The Productivity Paradox
While AI offers tremendous potential to boost developer productivity, it introduces challenges that require deliberate management. Developers report feeling more productive with AI tools, but there’s a concerning trend where organizations see developers accepting the vast majority of AI-generated suggestions without proper scrutiny. This suggests a fundamental breakdown in code ownership and future maintainability.
To address this, teams should establish clear guardrails for code complexity as one of the best practices. Set specific limits on function length, require developers to minimize cognitive complexity, and maintain strict standards around code duplication. These constraints ensure AI-generated code remains maintainable and understandable over time. Simple, readable code isn’t optional – it’s essential for long-term project health.
Another key aspect is documentation, which becomes even more crucial when working with AI-generated code. Teams should comprehensively document their projects with detailed design documents, architecture diagrams, and thorough project structure files. This documentation empowers the team to ensure that new AI-generated code aligns with the overall architecture while also providing the necessary context for AI systems to produce relevant and accurate suggestions.
Real Challenges with AI-Generated Code
Prioritizing speed through AI code generation can negatively impact overall code quality. Despite developers feeling more productive with AI tools, software delivery performance can decline, and code stability can be compromised. AI might produce code that works short-term – but also introduces subtle bugs, inefficiencies, or maintainability issues that accumulate over time.
One critical habit that must be established is eliminating unused code. While cleaning up old or deprecated code may not always be a top priority for developers, it’s essential for productive collaboration with AI. Those tools often generate unnecessary references and dependencies, which can create security vulnerabilities. Unused references can be exploited by malicious actors who may trick AI into including seemingly harmless dependencies that could later be weaponized.
AI models may also generate code with security vulnerabilities and this is an issue we must acknowledge and address. Since AI models are predominantly trained on existing open-source codebases, many of which contain biases, bugs, and vulnerabilities, AI-generated code can perpetuate or even amplify these issues, leading to unfair outcomes in software applications.
Last but not the least important is the human skill aspect, the overreliance on the AI and blind trust. In a study “Do Users Write More Insecure Code with AI Assistants?” from Stanford University it was proven that:
“developers who had access to the AI assistant were more likely to introduce security vulnerabilities for the majority of programming tasks, yet were also more likely to rate their insecure answers as secure compared to those in our control group”.
Additionally, junior developers may struggle to fully understand AI-generated code, which can lead to the introduction of code that is harder to maintain, debug, or modify. Over-reliance on AI code generation tools can also result in skill degradation, leaving developers less prepared to solve complex problems without AI assistance.
Building Systematic Quality Assurance
While adopting AI tools can significantly boost team productivity, organizations must also implement robust testing strategies tailored to AI-generated code. This includes establishing mandatory unit tests that are independent of the code generation process. It’s essential that the AI system responsible for writing the code does not also write its own tests or validate the code’s quality and security.
Given the volume and complexity of AI-generated code, traditional code reviews alone are insufficient to catch potential issues. Organizations need specialized tools capable of identifying and triaging complex bugs, security vulnerabilities, and licensing issues with third-party libraries at the same speed that new features are being developed.
Rigorous code review processes must become non-negotiable. Pull requests should fail if established best practices aren’t followed, and developers must have the tools and authority to address issues rapidly. This requires strong discipline from development teams and advanced tooling to support automated checks.
Additionally, teams must ensure that all third-party libraries recommended by AI are secure, up-to-date, and properly licensed. AI tools may suggest libraries without fully understanding their security status or licensing implications, which could introduce legal and security risks.
Raising the Standards
These practices are fundamental to software development, but with the widespread use of AI in coding, the expectations must be raised significantly. Best practices that were once considered optional are now essential. The code being written today will likely persist for years or even decades, making quality from the start absolutely critical. Organizations that establish strong habits around AI code management will build more maintainable, secure systems, while those that don’t will face the rapid accumulation of technical debt.
AI is here to stay and will continue revolutionizing software development, offering tools that enhance productivity, automate code fixes, and streamline workflows. However, regardless of how advanced these AI models become, companies must ensure that their code is secure, maintainable over the long term, and free from uncontrolled technical debt.
To achieve success, AI should be treated as a powerful tool that amplifies human capabilities, not as a replacement for human judgment and accountability. It’s important to establish a clear separation of concerns by using different AI tools for code generation and code assurance. This removes bias and provides a fresh perspective on the quality and safety of the code. By cultivating strong habits around developer accountability, code simplicity, comprehensive documentation, systematic testing, and rigorous review processes, organizations can harness the full potential of AI while avoiding its pitfalls. The key is to integrate these practices into daily workflows before issues start to pile up.
