How To Automatically Upgrade Rails And React Native Using AI

It’s well understood that keeping your Ruby on Rails and React Native code up to date is essential to keep your app stable and secure. Failing to do so can make your code ‌so outdated that it becomes risky to update it.

But let’s face it‌ — ‌manually handling these upgrades can be a pain. Each update forces you to dive deep into your code, carefully applying changes while making sure nothing breaks. This can be both time-consuming and stressful.

This is where using Artificial Intelligence (AI) can be a potential game-changer.

Using AI for updates and maintaining code can significantly ease this process, especially for large catalogues of software components or code blocks. By using AI for React Native and Rails upgrades, you can keep your software up to date and secure with less cost in time and effort.

In this blog, we’ll explore how AI can make it easier to keep your software dependencies updated and guide you through a step-by-step process to use it for upgrades. Plus, we’ll also talk about how Builder.ai is encouraging an environment where it’s easy to experiment using AI for upgrades and learning on the way.

Challenges in updating Rails and React Native manually

Upgrading Rails and React Native can be a daunting task, especially when you’re dealing with a large catalogue of code blocks. Here are some of the challenges that developers come across regularly 👇

Unique nature of each block

The biggest challenge with upgrading code blocks is that every one is unique. This variability means that a one-size-fits-all solution can't be applied to all projects, making the process even more time-intensive.

When done manually, every block in your codebase has to be carefully reviewed and updated individually. For instance, at Builder.ai, we have over 600 blocks; this would make it a very lengthy process.

Attention to detail

Upgrades require a high degree of attention. You need to thoroughly review release notes, update your application code to deal with incompatibilities and then test the app to make sure everything works as expected.

Even the slightest oversight can introduce bugs or security vulnerabilities, making this a high-stakes task.

Security and compatibility

Security updates are one of the key drivers for continuously being up to date with the latest frameworks that you’re using.

They not only keep your code protected against vulnerabilities but also help maintain compliance with security standards.

However, relying on manual updates can lead to delays, leaving your app exposed to potential security threats or incompatible with newer tools.

Managing dependencies

Dependencies are pre-made tools and libraries that are used to save time and effort while building an app. In Rails, you might use “gems”, while in React Native, you might use “npm packages”.

Managing these dependencies manually can be quite challenging when upgrading both Rails and React Native.

In Rails, for example, gems may face version conflicts, become deprecated and are no longer supported or introduce breaking changes that disrupt how your app works.

Similarly, in React Native, npm packages often face peer dependency issues where they need specific versions of other packages to work together or breaking changes after an upgrade, disrupting your app’s functionality.

Benefits of integrating AI into React Native and Rails upgrades

Using AI for upgrading React Native and Rails offers numerous benefits that enhance your code quality and streamline maintenance. Here are some of the key benefits we’ve gained using AI:

1 - Understanding the semantics of code

One of the most powerful features of AI in code upgrading is its ability to understand the semantics of the code rather than just the syntax. Simply put, AI doesn't just see a series of commands — it understands the structure and intent behind them.

For instance, in Rails, AI can recognise that a method is meant to perform a certain business function and adjust it accordingly during an upgrade.

Similarly, in React Native, AI can interpret component structures and workflows, which enables it to make changes that maintain your app's logic and flow.

This ability to understand the meaning of the code allows AI to handle even complex, non-standard implementations that manual upgrading might overlook.

2 - Easy to update large catalogues of blocks

Whether dealing with app’s backend components in Rails or app’s frontend elements in React Native‌ — ‌manually updating multiple reusable blocks can be error-prone.

AI enhances this process by automating updates across a large number of blocks at once. This means that instead of applying the same upgrade steps to each block individually, you can process the entire catalogue using AI.

It can apply upgrades in bulk while making sure that specific nuances in each block are accounted for. This allows for faster, more consistent updates.

Lastly, as your app grows, AI can scale effortlessly. This means you can manage more blocks without a proportional increase in manual effort.

3 - Handling fuzziness in code

AI excels at handling the "fuzziness" in code‌, like small variations and tricky edge cases that usually need a developer’s judgement. It can catch minor inconsistencies, figure out what you meant to do and make the right fixes without breaking the overall logic of your app.

For example, whether it’s adjusting a Rails route or resolving a React component import, AI can effectively navigate fuzzy code situations. This not only makes sure your app code remains functional but also enhances its stability and performance.

4 - Running and assessing updates

AI goes beyond simply applying changes‌ — ‌it can also run updates in a simulated environment and assess the quality of those updates. After applying upgrades, AI tools can test for errors, check for compatibility issues‌ and assess performance impacts and then automatically flag problematic areas.

For example, after applying the updates, the test suite for the block is run, and, if the tests fail, AI can look at the reason for the failure and perform another iteration of the updates, this time taking into account the reason for failing the first time.

This proactive approach not only saves your time but also enhances the overall reliability of the upgrade.

Step-by-step process of incorporating AI in code upgrading

Here’s a step-by-step process for incorporating AI into code upgrading for React Native and Rails 👇

1 - Take a sample of blocks

Start by choosing a handful of code blocks that represent the variety and complexity of your entire catalogue. This will make sure that the AI model can generalise well across different types of code.

In this step, you need to thoroughly document the selected blocks, including their current versions, dependencies‌ and any known issues. This documentation will serve as a baseline for comparison after the upgrades.

2 - Create a recipe of changes

Next, create a recipe for the changes you need to make. The recipe here refers to a set of instructions that a Large Language Model (LLM) will follow to perform the changes you need to make out the code.

To create the recipe, define the specific changes required, such as updating dependencies, refactoring code‌ and handling deprecated functions.

You also need to provide the AI with the necessary context, including the current state of the code, the target versions and any specific rules or constraints that must be followed during the upgrade process.

Remember, as React Native is for frontend and Rails is for backend, you need to create framework-specific recipes for each.

3 - Run the recipe on sample blocks

Apply the recipe generated to the selected sample blocks. This involves running the AI model on the code to perform the specified upgrades.

During this stage, you need to closely monitor the execution process to identify any immediate issues or errors that may arise. This’ll help you in making quick adjustments to the recipe if needed.

4 - Assess the quality of updates

After executing the recipe, use automated tests to verify that the upgraded blocks function as expected. This should include running unit tests, integration tests‌ and any other relevant test suites.

Don’t forget to conduct a manual review of the upgraded code to ensure that the changes are accurate and don't introduce new issues. This step is crucial for catching any nuances that automated tests might miss.

Finally, confirm that the upgraded blocks work as well or better than the original code.

5 - Iterate and refine the process

It's expected that you’ll see ‌variability in how AI applies changes, as it can sometimes introduce irrelevant modifications. You need to gather all the feedback you get from automated tests and manual reviews and use it to refine the AI model and upgrade the recipe.

Think of the upgrade process as a continuous cycle where you're constantly iterating and improving based on new insights and experiences.

6 - Scale the process

Once you’ve refined the process and proven its effectiveness on sample blocks, it's time to expand its scope to include the entire catalogue of blocks.

Aim to build a database of patterns and best practices that the AI can use to manage code upgrades across the entire organisation.

By doing this, you can apply the AI-assisted upgrade process to other projects beyond the initial blocks. This can include different programming languages and frameworks, making the process versatile and widely applicable.

As you continue to refine and expand this process, you'll be able to handle upgrades more efficiently and effectively across the board.

Builder.ai’s approach: cultivating an environment for AI experimentation

At Builder.ai, we are developing an environment that makes it easy to experiment with AI-assisted upgrades. We’re building a framework that enables us to try out new approaches, make mistakes and continuously improve.

While we face challenges such as variability in AI responses and making sure the right context is provided to AI for optimal performance, it doesn't deter us from our commitment to experimentation.

We are treating the incorporation of AI-assistance as an ongoing research project, where we are continuously improving our processes based on new insights and experiences. Our goal is to make mistakes quickly and at a low cost, learning from those mistakes and iterating quickly.

Looking ahead

By integrating AI into React Native and Rails upgrades, you not only speed up the process but also enhance its accuracy and efficiency.

And once you've achieved a certain win percentage, you can scale these improvements across your entire organisation. This will not only reduce manual effort but also ensures that your systems stay up-to-date with minimal disruptions.

At Builder.ai we’re developing a structured approach to integrating AI into our workflows, where we keep experimenting and learning to make those upgrades smoother and more effective.

If you're interested in how Builder.ai is leveraging AI to upgrade React Native and Rails, get in touch with us by clicking the banner below. 👇