From Continuous Integration to Artificial Intelligence

In the coming decade, all software is going to be created with the assistance of artificial intelligence (AI), whether that means the code itself is developed with the help of AI, or it’s tested and deployed using AI. 

This means that if you’re an engineer or engineering manager, it’s safe to assume that you have artificial intelligence in your future.

Artificial Intelligence is Imminent 

AI helps to solve common challenges all development teams are currently facing: limited resources, and the cost and time it takes to hire and onboard new developers. AI enables teams to move faster using the resources they already have. 

Furthermore, artificial intelligence introduces the opportunity to automate tasks, particularly for common coding tasks that are highly repetitive. 

Recent years have seen the rise of powerful machine learning (ML) models trained on massive amounts of data that can be used to generate text and images using simple prompts, as shown below with the prompt written in text.

Engineering teams can take these ML models to train over code retained from open source repositories or your organization’s repos, changing the way you think about software development. 

Artificial Intelligence Enables Faster Innovation

In the past decades, organizations have realized that having a solid source control and CI/CD (continuous integration and continuous delivery) pipeline is essential for shipping high-quality software. 

As you and your team go through the pipeline, you accumulate a lot of information and software records. First of all, you have the codebase, which hopefully contains code that has been tested and reviewed to ensure it contains your organization’s coding patterns and best practices. You also have test cases that historically run on various versions of your code that either crashed or passed successfully, and a lot of information from past test executions and past code reviews and comments. You have all of this rich information, so why not reap the benefits and learn from it in order to accelerate all stages of the software development lifecycle?

You can think of this as having another layer in your system: an AI layer that is learning from all the data you’ve accumulated throughout the stages of the software development lifecycle to identify opportunities for accelerating each stage moving forward. 

Artificial Intelligence + Human Guidance 

If you empower the AI and machine learning models with human guidance, the model can capture beyond what it has learned from evaluating the codebase and understand what the human experts already know to propagate expert guidance. 

Even when the code has already been written, the AI can recommend ways to improve the code with things like automatic refactoring. Test generation allows the machine to learn both from the codebase and previous tests to generate more unit tests, or larger tests, etc. And AI code review can be done in a merge or pull request or directly in the IDE in real time. 

Automating Code with Artificial Intelligence

Artificial intelligence is the first real innovation in terms of accelerating the process of writing code. Generative AI models, notably large language models trained on millions of open source projects to predict code, work in all IDEs and for all languages. 

Typically, generative AI models can automate up to 30% of the code. This helps make developers be more productive, happier, and improve their ability to code because the AI is suggesting common patterns, helping to reduce error. Plus the developer no longer has to context switch to another solution depending on what state of the lifecycle they’re working on. 

AI makes developers happier because it automates the mundane parts of coding–the parts of the work that few devs actually enjoy–and gives them more time and energy to think and code creatively to solve problems. 

Developer Happiness Leads to Greater Productivity

Just as artificial intelligence and automating code can lead to better developer experience, so too can upgrading the tools in your tech stack. GitKraken develops three tools that help make developers and teams more productive using Git: GitKraken Client, GitLens for VS Code, and Git Integration for Jira.