AI Code Review Experiment in Laravel with Cursor & Claude

Discover an AI-assisted code review experiment in Laravel using Cursor and Claude 3.7, exploring refactoring and automated tests.

This article explores an innovative experiment conducting an AI-based code review in a Laravel project. The focus is on leveraging AI tools to refactor code, generate automated tests, and enhance development workflows. By examining the process from reviewing routes to modifying controllers and applying best practices, the post reveals valuable insights for developers looking to harness AI code review and optimize Laravel applications.

🚀 ## 1. Experiment Setup and Methodology

Imagine a developer sitting at a cluttered desk, staring at a tangled web of legacy Laravel routes and controllers—a scenario as common as it is frustrating. Now, picture that same developer harnessing the power of modern AI to not only review but intelligently refactor the code. This is not a science fiction tale but a real-world experiment where AI tools, like Cursor for quick code suggestions and Claude 3.7 for deeper analytical insight, are thrown into the mix to revolutionize the workflow. The goal? To transform a rusty junior project without writing code from scratch—a daring exercise that blends human intuition with algorithmic precision.

In this experiment, the undertaking was to apply AI as a “code reviewer” rather than a mere code generator. Developers often grapple with technical debt in projects built over time, and the idea of letting AI scan, suggest improvements, and even refactor code aims to restore sanity in complex codebases. The experiment was broken down into several stages. First, the developer began by directing the AI to review the routes file—a central piece in any Laravel application that maps HTTP requests to controllers. By feeding the file into Cursor and asking, “You’re a senior developer, what would you change?”, a series of suggestions were spun out in nearly real time. This process captured both the promise and pitfalls of AI-assisted programming. The AI proposed a seamless migration from anonymous route closures to a more maintainable controller-based structure, pointed out unreachable code in laid out conditional statements, and recommended the integration of middleware—hallmarks of modern Laravel practices as outlined on the Laravel website.

The experiment’s methodology capitalized on a “vibe coding” approach—a technique where developers allow AI to produce results from blind prompting, assessing its reaction under minimal context. This meant that instead of carefully designing each prompt, the AI was placed in a creative scenario: generate a code review as if you were an experienced developer. The inherent unpredictability of this method is both its charm and its challenge. During the review, the AI took roughly five seconds to generate a set of recommendations. However, unforeseen issues soon emerged, such as Cursor freezing during input-heavy sessions. This glitch, likely due to the constraints of the free version of Cursor, underscored the practical trade-offs when deploying AI at scale. For further insights on debugging and performance issues in coding environments, see Martin Fowler’s refactoring principles.

Beyond the technical specifics, what truly stands out in this experiment is the process—the iterative feedback loops, the blending of automated suggestions with manual corrections, and the ever-present safety net of automated tests. The AI not only suggested improvements but also generated a set of automated tests for both GET requests in the new route file and specific endpoints in the login controller. In one instance, after refactoring the login controller to include improved form requests and updated syntax, the developer executed these tests using PHPUnit, confirming that the new code behaved as expected. The automated tests served as a crucial checkpoint; they were the guardians ensuring that while the code was being modernized, its functionality remained intact. This continuous validation mirrors the best practices advocated by the Git community and reflected in comprehensive test-driven development strategies outlined in various industry publications like those on DZone.

The experimental setup was a symphony of modern techniques. The developer adopted a step-by-step methodology:

• First, initiate a code review using blind prompting to simulate the environment of a seasoned developer.
• Second, apply AI-generated suggestions on the routes file, ensuring the transitions from closures to controllers are smooth.
• Third, employ AI to generate corresponding automated tests and identify discrepancies (for instance, missing factories leading to test failures).
• Finally, iteratively refine the code by integrating manual corrections and second-round prompts where the AI’s suggestions met unforeseen challenges.

This process was not just about using AI to rewrite code—it was an exploration into the evolving relationship between human oversight and machine intelligence. It underscored the potential benefits of AI in software development while keeping a steady reminder: AI-generated code always requires rigorous human review and testing to meet production standards. For a deeper dive into optimizing software development with AI, refer to the thought leadership on InfoQ.

🧠 ## 2. AI-Powered Refactoring in Laravel

In the realm of Laravel development, refactoring is both an art and a science. Consider a scenario where the routes file—often the first point of contact in a Laravel application—is cluttered with route closures, outdated middleware applications, and unreachable code. Traditionally, cleaning up such a file would demand painstaking manual rewrites. Instead, the experiment pushed AI to its creative limits: can it cascade a series of improvements across major components of a Laravel project? The answer, as the experiment revealed, is a mix of promise and caution.

The initial step was to transition from route closures to controller-based handling—a best practice advocated by the Laravel News community for improved code maintainability. The AI, guided by prompts phrased in a casual “blind coding” style, recognized that having anonymous functions in routes can obscure the logic, especially when the codebase scales. By suggesting the extraction of logic into dedicated controllers, the AI aligned the code with modern Laravel architectural principles. Additionally, it flagged unreachable code segments where conditional statements in routes ended with a final “return” that would never execute. This detailed review not only reflects the AI’s capacity to identify code smells but also highlights an important lesson from Refactoring Guru: recognizing and removing dead code is critical for long-term code health.

A particularly engaging part of the refactoring process was seen in the handling of the login controller. Here, the AI suggested multiple enhancements:

• Transitioning from outdated syntax – The AI replaced deprecated styles from Laravel 7 with updated, more expressive syntax compatible with modern practices.
• Incorporating middleware – It ensured that the controller’s logic was executed within the correct middleware context to secure the application appropriately.
• Implementing route model binding – By leveraging Laravel’s ability to automatically inject models based on route parameters, the AI streamlined the controller’s code.
• Utilizing form requests – To enforce data validation rigorously, the AI generated dedicated form request classes that offloaded validation logic from the controller, enhancing readability and maintainability.

The process of refining the login controller was rich with human-like decision-making. As the AI-generated code improvements were applied, the developer noted the importance of incremental development—committing changes frequently to preserve a record of what the AI had induced. This practice resonates with the principles of version control and underscores the value of using tools like Git. Real-world development scenarios often mandate that every refactoring step be followed by rigorous testing. In this experiment, after the AI-implemented changes, automated feature tests were generated to verify that each route and controller endpoint responded with the expected HTTP status codes. The tests, although initially resulting in a cascade of 29 failures due to missing factories and incorrect naming conventions, ultimately served as a reminder that an AI’s work must be tempered by the realities of real-world codebases.

The refactoring of the routes file itself also provided several learning points:

• Middleware consistency: AI suggestions included adding middleware to ensure only authenticated users could access certain routes. This aligns with security best practices detailed on OWASP.
• Resourceful routing: The AI recommended consolidating similar routes into a resource route where practical. This refactoring method enhances code clarity and leverages the full power of Laravel’s routing capabilities.
• Elimination of unreachable code: Through a keen eye for detail, the AI identified code segments positioned after fallback cases that, by definition, could never run. Removing such code not only cleans up the file but also reduces cognitive load for future maintainers.

A noteworthy moment came when the AI was tasked with generating tests for GET requests. The automated tests were designed as smoke tests—ensuring every endpoint returned a successful response while checking for potential misconfigurations in named routes. Despite anticipating a smooth run, the tests flagged multiple failures owing to mismatches in route naming conventions and missing factories. This discrepancy reiterated a critical point: while AI can swiftly generate and refactor code, it may struggle with context-dependent nuances without a comprehensive understanding of the project architecture.

Throughout the refactoring process, several key lessons emerged. For instance, when generating missing elements like factories for the Admin model or notifications, the AI had to deduce the appropriate structure from existing models and migrations. It was a reminder of how essential context is for generating relevant code, much like how the theoretical foundations in software engineering dictate best practices. Additionally, the experiment uncovered that despite AI’s impressive capabilities, there exists a gap between generating syntactically correct code and ensuring its semantic correctness within a complex ecosystem.

The transformative potential of AI-powered refactoring in Laravel shows that an even blend of automated assistance and human oversight can yield impressive gains in productivity and code quality. During this phase of experimentation, it became clear that to effectively harness these tools, developers must adopt a mindset of continuous integration: always commit changes, run tests frequently, and be ready to intervene manually when the AI’s suggestions stray off course. For more insights into balancing automated workflows with human review, consider reading articles on Smashing Magazine.

🎯 ## 3. Challenges, Learnings, and Best Practices

In any pioneering experiment, challenges are inevitable. This AI-assisted refactoring journey was no exception. As advanced as AI tools like Cursor and Claude 3.7 are, their free versions and inherent limitations revealed critical lessons on how to effectively integrate AI into development workflows. The experiment highlighted several challenges that resonate deeply with the broader developer community, particularly those engaged in code refactoring and technical debt reduction.

One of the most immediate issues observed was the inconsistency in AI performance. During the review of the routes file, for example, Cursor was observed to freeze—lagging for a full minute without delivering any output. This sporadic freezing, likely a product of the tool’s free-tier limitations, impaired workflow productivity and underscored the trade-offs between cost and performance when utilizing AI in development. It’s a practical reminder that while AI can be a powerful ally, its limitations must be acknowledged and planned for. Resources such as VentureBeat provide further discussion on the challenges and opportunities in AI technology deployment.

Another prominent challenge was reconciling AI-generated recommendations with existing code conventions. AI, operating on blind prompting—or “vibe coding,” as it was humorously dubbed—often delivered suggestions that were logically sound but not always aligned with the specific context of the application. For instance, when refactoring the routes file, the AI’s alteration of route names and the application of middleware sometimes clashed with the established naming conventions and grouping defined earlier in the application. Resolving such discrepancies required manual intervention, a process that involved scrutinizing each suggestion and making on-the-fly corrections. This interplay between AI automation and human judgment is reminiscent of the Harvard Business Review’s insights on balancing technology with human oversight.

Beyond these technical challenges, the experiment also offered a trove of learnings that should inform future AI-assisted coding endeavors. One critical insight is the importance of maintaining robust automated tests throughout the refactoring process. When the AI modified the code, automated feature tests were run to validate that the GET requests across various routes returned the expected responses. Despite initial excitement over the AI’s capability to generate tests on the fly, these tests exposed several gaps—ranging from missing factories for models like the Admin and Notification classes to route names that didn’t match the revised patterns. These test failures served a dual purpose: they provided immediate feedback on the areas where AI guidelines had faltered, and they underscored the indispensable role of automated testing in ensuring code integrity. To learn more about best practices in testing, developers can check out Selenium and other resources on automated test frameworks.

The experiment also revealed subtle aspects of workflow management that are crucial when working with AI-generated code. For example, one piece of strategic advice that emerged was to commit changes frequently. In a traditional coding environment, developers might lean towards larger, monolithic commits. However, when working with AI—where every suggestion is subject to potential drift—it becomes essential to capture the state of the codebase at every significant step. Frequent commits make it easier to trace back the origins of a bug or an unintended behavior, a notion that aligns with the advanced version control strategies popularized by Atlassian.

Furthermore, the experiment illuminated a range of best practices for leveraging AI in development:

Best Practices for AI-Assisted Refactoring

• Commit Often: Maintain a habit of committing changes after every significant AI intervention. This not only provides a safety net but also creates a clear history for audits, as advocated by Git’s best practices.
• Validate Outputs Continuously: Always run automated tests after applying AI-generated changes. This ensures that even if the AI introduces syntactical improvements, the semantic correctness of the code remains intact.
• Integrate Human Review: AI is a powerful tool but not infallible. Pair AI suggestions with manual code reviews to catch nuances and context-specific issues. For a deeper understanding of code reviews, look into JetBrains’ insights.
• Start Small: When adopting AI for code refactoring, begin with isolated components (like a single routes file or controller) before scaling the solution across the entire project.
• Use Dedicated Channels for Experimentation: In scenarios where multiple topics compete for developer resources (as was the case when the channel limited AI-related videos to weekends), explore the possibility of a dedicated channel or blog for AI and Laravel innovations. This strategy not only helps in managing content flow but also in creating a focused community. For more on managing digital content strategies, consider reading Content Marketing Institute resources.

Reflecting on the Broader Impact

The trial of AI-assisted code refactoring in Laravel was not just a technical experiment—it was a window into the future of programming. As developers continue to adopt AI-driven tools, there is an emerging need to foster a balance between automation and the human touch. The experiment showed that AI can streamline mundane tasks, suggest modern coding practices, and even generate boilerplate code (like those elusive factories) with admirable speed. However, when the AI misfires—such as introducing incorrect route naming conventions or failing to account for legacy code dependencies—the responsibility still falls on human developers to iron out the differences. This dynamic synergy is at the heart of OpenAI’s vision of augmenting human capabilities rather than replacing them.

It’s also a lesson in embracing imperfection. Developers experimenting with AI will inevitably encounter moments where the tools either freeze, generate incomplete suggestions, or demand tedious manual corrections. These hiccups are not setbacks but stepping stones to refining how AI is integrated into everyday workflows. Much like the early days of integrating any groundbreaking technology, patience, continuous feedback, and iterative improvement are key. For additional discussion on managing technological transitions in software development, check out the articles on TechCrunch.

Looking to the Future

There is a clear and exciting trajectory for AI-assisted workflows. As AI models become more sophisticated and integrate deeper contextual awareness, the gap between automated refactoring and human expertise is bound to close. Future updates to tools like Cursor and Claude could eventually provide more consistent outputs, drastically reduce errors like freezing or incomplete suggestions, and even offer real-time collaboration features that blend seamlessly with traditional version control. For cutting-edge news on AI advancements in software development, Wired offers regular updates.

This experiment on AI-powered code review and refactoring in Laravel illustrates that while the technology is promising, its current state demands a cautious approach. The best practices gleaned from this journey—commit frequently, validate continuously, and always ensure human supervision—are not just steps in a process; they are the building blocks for a future where AI and developers work together seamlessly. If nothing else, this experiment serves as a testament to the relentless pace of innovation and a reminder that in the realm of tech, embracing change with skepticism and optimism is the optimal way forward.

In summary, this journey through AI-assisted Laravel refactoring has been as instructive as it has been experimental. The integration of tools like Cursor and Claude 3.7 provided a glimpse into the next frontier of software engineering while highlighting the resilience and creative problem-solving skills that define effective development teams. As the industry evolves, developers are encouraged to experiment boldly but verify meticulously—ensuring that every AI-induced change not only cleans up the codebase but also builds towards a robust, future-ready architecture. With the right guidelines, strategic oversight, and an appetite for continual learning, the successful marriage of automation and human ingenuity is not a distant dream but an attainable reality.

Through this exploration, it becomes clear that AI is not a magic bullet but a powerful complement to traditional development practices. As AI tools mature and their integration becomes more refined, the possibilities for enhancing productivity and elevating code quality will expand dramatically. Meanwhile, developers must remain vigilant and adaptable, always reviewing, testing, and committing changes as part of a disciplined, iterative process—much like the incremental improvements that have defined modern software development since the advent of version control systems like Git.

Ultimately, the experiment is a call to action for organizations and developers alike: explore how AI can empower your workflows, innovate responsibly, and build the future of code together. For more detailed strategies on integrating AI into development pipelines, MIT’s publications on emerging technologies offer a wealth of in-depth analysis and forward-thinking perspectives.

By combining the analytical power of AI with disciplined human oversight, Rokito.Ai positions itself at the intersection of innovation and productivity—setting the stage for a future where code is not only written faster but smarter, safer, and with a clear vision for long-term success.