How AI Is Changing the Way Developers Write Code

Remember when GitHub’s CoPilot first made waves by automatically generating source code in 2021? It all seemed like a great way to streamline the process until CCS researchers found that 40% of the code generated was highly vulnerable as it included design flaws and bugs.  

But GitHub quickly learned from their mistakes — and so did numerous other AI coding models. 

Now, artificial intelligence (AI) plays an increasingly large role in software development. Developers utilize AI throughout every coding cycle phase to streamline the process, improve software functionality, and create more complex and multifaceted programs. Here’s how this technology has become a game changer that actually does the job.

Generative AI in Coding

With generative AI's rapid advancements and increasing reliability, many developers use platforms like Amazon CodeWhisperer, Tabnine, and the updated version of Copilot to automate their coding workflows. Tools such as machine-learning-based security checks are also being used to assess CI/CD pipelines.

Overall, GenAI has some unique capabilities for writing code. Its automation abilities can simplify development tasks such as formatting new code, validating code, and fulfilling commit and pull requests. Its rule-based structure can streamline tasks like manually scanning SQL injections and automatically scanning through code and identifying patterns within minutes. 

GenAI’s machine learning algorithms can also assist with tasks like tracking security vulnerabilities. ML models can be trained to identify known SQL injections and spot patterns in unfamiliar injections. This capability assists developers in recognizing existing vulnerabilities and enabling them to predict and mitigate new vulnerabilities. Additionally, machine learning models can predict and generate everything from coding sequences to novel solutions and answers. 

Overall, these tools reduce the frequency with which developers need to search for boilerplate code and write new code, effectively transforming their roles in the coding process. Instead of spending the majority of their time writing code, they can spend more time making strategic decisions, optimizing existing code, and developing creative software solutions.

Integration of AI Assistants

LMs such as OpenAI’s GPT-4 form the foundations for AI coding assistants by powering the underlying technology of tools designed to produce functional computer code. These models have been trained on vast amounts of programming languages, documentation, and real-world code examples, which allows them to understand the intent behind a developer’s prompt and the structure of well-formed code.

Tools like GitHub Copilot build on this foundation by actively assisting developers in real time. As developers type, Copilot monitors their keystrokes and context within the file. It then uses GPT-4’s predictive capabilities to infer what the developer is trying to achieve, whether writing a function, debugging, or completing a class. Based on that understanding, it generates relevant and usable code snippets on the fly.

Other AI assistants, like Meta’s Code Llama and Stability AI, offer a range of capabilities engineered for developers. Code Llama provides a free code-generating model for developers of all skill levels, Stability AI’s StableCode assists developers in automating the coding process, and models like ChatGPT can help write, optimize, and debug code.

These code assistant programs give programmers the information they need to code effectively and address common issues. This data appears in real time alongside the code they’re writing, saving them from having to scan forums and websites for solutions. AI assistants track developers’ code and notes penned in natural language across all of the files linked to a project, ensuring all AI code produced is aligned with the nature of the task. 

Deep Learning and LLMs

As a field of machine learning, deep learning has become invaluable for developers. It trains artificial neural networks with intricate layers of data, enabling AI tools to extract comprehensive representations from vast, raw datasets. 

Large language models (LLMs) have also prompted notable advancements in AI, natural language processing (NLP), and coding. These models can understand natural language inputs and generate human-like text and code. Their outputs are based on the massive volumes of data they have been trained on.

Both these tools are being used to assist in code completion. AI code generation platforms use deep learning and LLM models to offer developers code suggestions and completions in real time. Many tools, including Copilot, can be integrated with widely used code editors. Once integrated, they assist programmers by suggesting code snippets, completions, and entire functions based on each project's intended functionality and context.

These platforms use data from private and public code repositories to enhance their suggestions and capabilities over time. As they learn, they generate increasingly more contextually accurate code for developers. Most tools support an extensive range of programming languages, ranging from Python, Java, JavaScript, and C++ to C#, TypeScript, Rust, Kotlin, Perl, R, and others.

The Collective Benefits of AI for Coding

Generative AI platforms, AI assistants, and deep learning models all offer key benefits for coding workflows. 

Increased Productivity

The primary benefit of these tools is their ability to enhance developers’ productivity considerably. Each of these tools can work together to accelerate software development. They reduce the new code that developers must write from scratch by suggesting accurate and relevant snippets and code completions as they work. 

Faster Deployment

These tools can reduce the burden of time-consuming, repetitive tasks through automation and decrease deployment times concurrently. Intelligent assistants can help coordinate tasks between developers, generate build scripts, and automate testing and deployment pipelines. They also help with debugging, translating code from one programming language to another, and refactoring old code into a newer optimized version. This speeds up release cycles and reduces human error while freeing up developers to focus on innovation, problem-solving, and functionality instead. 

Enhanced Collaboration

With 28% of the global workforce working remotely, many development teams operate across different time zones. In this case, AI coding assistants enhance global collaboration, ensuring consistent workflows, shared coding standards, and seamless task management, regardless of physical location.

Ongoing Upskilling

Developers can use these AI functions to learn more about coding and enhance their knowledge and skills. AI platforms provide a wealth of educational resources for beginners and seasoned programmers by granting access to proven coding patterns and practices and simplifying industry terminology. Even skilled professionals can use AI to bridge gaps in their coding knowledge and effectively use new programming languages.

Summing Up

As the functionality of AI web dev tools advances, AI is expected to play an even greater role in creating user-friendly, versatile software. In a few short years, we’ve gone from buggy, unsafe, and untrustworthy code to code that ticks all the right boxes at the click of a button. 

However, this low-code approach doesn't take developers' work away from them. It’s giving them the opportunity to streamline workflows and wrap up projects faster and more efficiently.