By Juan Soriano, Senior Developer

As generative AI technology improves and access to tools becomes increasingly democratised, its ability to streamline processes and increase efficiency is proving game-changing for mobile development teams of all sizes.

From automating manual tasks to improving customer experience within mobile products, as a team we’re exploring as many use cases for generative AI as possible to support Novoda and our clients.

So, what are the core use cases we’ve found for AI within Mobile DevOps?

Automatic Documentation

It is incredibly important for mobile devs to document everything they build, but this can be an arduous and time-consuming process with the output varying in quality.

Generative AI tools such as Mintlify, which read code and automatically create explanatory documents, not only automate the documentation process for devs but also ensure the quality of documentation produced.

What’s more, automating processes such as documentation gives your devs more time to work on the more enjoyable and valuable parts of their job, increasing overall efficiency and enabling your mobile teams to deliver greater value. 

Code Review

In the same way that any language has a specific grammar and syntax, code has its own set of language rules too. Because of this, AI tools can be programmed effectively to read through code and pick up the smallest mistakes faster and more accurately than any member of your team. 

Using AI such as Amazon CodeGuru to review code before it passes to a team member not only speeds up the process but catches the tiny mistakes that might otherwise be missed and impact testing later down the line.

User Testing

To effectively test code before deployment, dev teams need to consider all the possible different scenarios a user could face – which is tedious and time-consuming. 

Right now, AI-powered user testing tools are being developed which will be able to generate a large number of the specific tests you need to perform in a fraction of the time – and for a fraction of the cost – that traditional user testing would take. 

For now, we would still recommend supplementing any AI tools with proper user testing, but we think it’s realistic to assume that within a year, user testing could be fully AI-powered.

Ticket Classification & Creation

When working on any mobile development project, robust classification of tickets is essential, making sure that the category and complexity of each ticket are correctly identified before being assigned to the correct individual or team. 

Problems with this process can arise when there are different people across multiple departments, as well as users, creating tickets – particularly if the creator isn’t familiar with the language used by devs.

AI language models can take a history of tickets which have been written in the past, and use this to rewrite tickets which are submitted in layman’s terms into a developer brief which can then be easily categorised and assigned appropriately. This frees up time that’s being spent ping-ponging tickets between different departments or seeking further clarification. 

Knowledge Sharing Between Teams

As your company and mobile development teams scale, onboarding new team members becomes an increasingly difficult challenge – and generative AI can help here too.

By developing a ChatGPT-style interface that learns from team infrastructure and documentation, new team members can easily get answers to common questions like:

• Who within the organisation do I need to speak to regarding this particular problem?
• What piece of code is related to my current challenge?
• How has this problem been resolved in the past?

This means your senior team members waste less time repeatedly answering the same questions or pointing new starters in the right direction. It also allows your teams and departments to work together more effectively – especially in the case of remote and hybrid teams.

Integrating Generative AI Into Products

As well as leveraging generative AI to streamline operations and processes, familiarising your mobile development teams with the available capabilities, they’ll inevitably start thinking about how to integrate AI into the products they’re developing.

For example, food delivery apps might use generative AI to make more personal and specific recommendations to users based on previous ratings, activity or dietary needs without them needing to trawl through the app to find what they’re looking for. 

It’s an exciting time to be working in the Mobile DevOps space, as the capabilities of AI are growing every day. As more and more developer teams embrace AI within their internal processes and product work, if you’re not starting to use AI now in the use cases outlined above, it won’t be long before your organisation is left behind.

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