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Developers are in a tough spot. The growing dependence on technology means businesses rely heavily on software. Consequently, developers must be able to guarantee the security and quality of code their software operates on — while producing even larger volumes of code.
This increased workload means developers need to work quickly. Developer velocity — the speed and efficiency they can create, modify and deploy high-quality code — is imperative for business success. Increasing developer speed can be attained by integrating effective code development tools, automating code analysis and employing techniques like automation, code completion tools and CI/CD pipelines. Not only can these tools and techniques improve quality and production time, but they can also boost morale at a time when developer burnout is nearly universal.
The resources developers have at their fingertips today already help them work smarter, not harder. And AI technology stands to increase developer productivity even more. Adding generative AI coding assistants to the developer workflow helps them get even more out of a busy workday while producing high-quality code.
Generative AI coding assistants use machine learning (ML) and large language models (LLMs) trained on big data sets of code and natural language. They are powerful tools to help software developers create code, recommend improvements, automate operations and handle many other aspects of the coding and development process. Generative AI coding assistants don’t only complete code; they do so with snippets or segments based on natural language prompts or partial code inputs.
Solutions like GitHub Copilot, built on OpenAI’s Codex model, support developer teams by writing code at a greater speed than a developer could independently. By taking care of some of the tedious parts of a project, generative AI coding tools also allow developers to focus on the parts that require more concentration and effort.
However, while generative AI coding assistants can greatly benefit and support developers, they cannot be full replacements for the human ability to think critically. Code written by these tools should always undergo a thorough review to ensure the output produced aligns with project requirements, adheres to quality standards and doesn’t degrade security.
These tools make a powerful ally in the fight to boost developer velocity in a variety of ways, including:
AI-generated code can swiftly produce code snippets, algorithms and complete modules from high-level descriptions. They can convert ideas into prototypes for faster ideation and increased creativity. This allows developers to bring several potential solutions to a problem and see how each can be applied. Having a wide variety of options promotes problem-solving and allows exploration of ideas that aren’t immediately obvious. Once developers have chosen the desired solution, they can take a simple version of that code and further adapt and merge it.
A lot of coding is tedious, monotonous and even boring — but AI coding assistants can help. These tools can automate processes like boilerplate code setup or environment configuration by using predefined templates for prevalent coding patterns, classes or functions. Developers save time, and the resulting code is standardized.
The rapid creation of code through AI coding assistants decreases the development cycle and increases the volume of code generated. But this contributes to a greater need for code reviews, testing and feedback. Solutions like SonarQube and SonarCloud can facilitate faster feedback and reviews to generate clean code that lives up to company standards.
Many AI coding assistants aren’t limited to a specific programming language or framework, giving them greater flexibility and freedom to create prototypes across them. That variety is especially useful in diverse environments where developers might be more skilled in different languages.
Even if AI coding assistants can’t process complex code well, they can still help simulate certain scenarios for troubleshooting and help developers understand the potential consequences of different coding choices. This can help them process and simplify solutions to problems.
Coding AI assistants like Copilot and Amazon CodeWhisperer are available as IDE extensions, which means they become a natural part of the developers’ workflow, increasing productivity without much interruption. To maximize the quality of AI-generated code, SonarLint IDE extension can check the code being developed on the fly and immediately fix issues in your local environment. The SonarLint–AI coding assistant combo helps ensure quality code generation in real time.
AI coding assistants can generate and update technical documentation, a critical but often tedious task that is important for project management and scalability. These tools can explain and document your code through comments describing what a section is meant to do and how it fits into the project overall. It increases the context of the code you’re producing.
AI coding assistants offer significant benefits in terms of improving velocity for developers, but code still needs human oversight before it’s deployed into production. Teams can never assume that what AI produces is foolproof without checking it themselves. To maximize gains from these tools and ensure code quality, security, maintainability and reliability, organizations must prioritize careful review, testing and analysis.
Only by putting eyes on what AI coding assistants build can developers be confident that code is clean code — code that’s easily understandable, portable and capable of changing through structure and consistency. This kind of code results in maintainable, reliable and secure software to meet ever-growing performance demands. In today’s competitive landscape, quality, secure software has become table stakes for businesses striving to succeed.
Generative AI coding assistants have the power to transform the development process when used correctly, and developers must take advantage of these tools to keep up with business demands in today’s world. But employing solutions that reduce risk, ensure quality and extract more value from code alongside these tools is the only way to get the most from them and to see the results companies want — and need — to achieve.
