AI coding assistants help developers write, review, and test code more quickly. You describe what you need in simple language, and the AI suggests or generates code. We are discussing the best AI tools for coding here. These tools support human developers but do not replace them. They boost productivity and improve code quality while leaving creative problem-solving to people. An AI coding assistant can complete lines of code, create functions from prompts, find errors, or write documentation for your code.
Using AI in software development is common now. A 2024 Stack Overflow survey found that 76% of developers are using or planning to use AI coding tools. In the survey, 81% of respondents said these tools made them more productive, 62% said they helped them learn faster, and 58% said they improved their workflow.
However, developers are still cautious about AI suggestions; only 42% fully trust the output, so it’s important to review it. In short, AI coding assistants help programmers see their code more clearly. They catch errors and handle repetitive tasks, allowing people to focus on design and logic.
Benefits of AI Tools for Coding
AI coding tools assist developers by making their work easier. They automate repetitive tasks, such as writing boilerplate code, completing common patterns, or even creating whole modules from a description. This means developers don’t have to type as much. IBM says that by handling routine tasks, tools like generative AI and code completion reduce the need for developers to write code, fix bugs, or do long tests. This results in faster development and fewer mistakes.
Higher Productivity:
A study from Stack Overflow found that most developers say AI assistants help them code faster. By automating simple coding tasks and providing suggestions, these tools enable developers to focus on more complex problems.
Learning and Documentation:
AI tools can explain code and create documentation automatically. If you are learning a new API or programming language, an AI can tell you what a piece of code does. It can also update the documentation as the code changes. This is particularly useful for junior developers who receive immediate help from the AI, or for teams that need clear and consistent comments.
Improved Code Quality:
AI can find bugs or style issues in your code. Many tools use static checks or security scans to highlight problems as you code. By catching issues early, AI assistants help create cleaner and more optimised code with fewer errors.
More Innovation Time:
When developers offload dull tasks, they can focus more on creative work. An IBM article notes that AI transforms engineers from “code implementers” to strategic designers and problem-solvers. Teams can use the extra time for planning, developing new features, or collaborating instead of handling repetitive work.
AI tools speed up coding by allowing quick feature prototypes, instant updates to old code, and fast answers to questions like “how do I do X?” IBM notes that engineers no longer need to search through documents or Stack Overflow; they can ask an AI for relevant answers. However, developers remain cautious and, according to a Stack Overflow survey, 66% of teams are wary of fully trusting AI, making expert judgment important.
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Common Tasks and Use Cases:
AI coding assistants cover a range of programming tasks. Some of the most common are:
Code generation and autocomplete:
AI can help write code based on your needs. For example, if you say “create a function to sort an array,” the tool can complete the function details. This works like smart autocomplete. GitHub Copilot is one tool that offers this feature and can predict your next move.
Debugging and testing:
AI assistants can review code, identify possible bugs, and create test cases. Instead of writing all your tests by hand, you can describe how a function should behave, and the tool will generate unit tests that cover different scenarios. This catches mistakes you might overlook and saves you time on repetitive testing tasks.
Code review and documentation:
Some AI tools help with reviewing pull requests or writing documentation. They can summarise changes in a pull request, point out security issues, and suggest documentation for functions. This makes code reviews faster and keeps project documents up to date.
Imagine adding a new feature. You create a simple prompt for the AI, which writes the initial code. You make adjustments, and it generates unit tests. Another AI reviews the code, noting edge cases or security issues. This way, AI tools assist with various parts of development smoothly.
Popular AI Coding Tools
Many tools exist today, each with its own strengths. Some well-known examples include:
GitHub Copilot:
GitHub and OpenAI created Copilot, an AI tool that acts as a programming assistant in code editors like VS Code and JetBrains IDEs. It provides real-time suggestions for code, understands context across multiple files, and even has a chat feature to explain code. Copilot can create code based on comments, suggest complete functions, and help write documentation. It supports many programming languages and can use models like GPT-4, Claude, or others to generate suggestions.
Tabnine and Codeium:
These tools help you complete your code faster by suggesting what to type next. They work with almost any IDE and use AI to make predictions. For example, Tabnine is focused on privacy because it can run on your own machine, keeping your code safe. Codeium is a free option that offers unlimited suggestions. Both tools help you avoid typos and reduce repetitive coding in different programming languages.
Amazon CodeWhisperer (AWS Q Developer):
Amazon’s AI coding assistant, now known as Q Developer, integrates seamlessly with Amazon Web Services (AWS) and popular IDEs such as Visual Studio Code and IntelliJ IDEA. Similar to GitHub Copilot, it enhances coding by providing intelligent autocompletion, real-time error detection, and contextual suggestions. A key feature is its ability to generate AWS infrastructure code directly from developer comments, which accelerates cloud-based application development. Overall, Q Developer is specifically tailored for cloud development, helping developers focus on quality coding while automating routine tasks.
Replit Ghostwriter:
Ghostwriter is an integrated feature of the Replit online IDE, specifically designed to support educational purposes and facilitate rapid prototyping. It allows users to generate code and receive explanations interactively within a web-based environment, making it an excellent tool for both learning and conducting quick demonstrations.
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JetBrains AI Assistant:
JetBrains has integrated its own AI capabilities into its development environments, such as IntelliJ and PyCharm. These features include advanced code completion, refactoring suggestions, and documentation generation, all designed to enhance the development experience directly within the IDE. Notably, JetBrains prioritises data privacy and offers the option to utilise local AI models for users who prefer that approach.
Many tools use large language models (LLMs) like GPT-4 or Claude, as well as special models. Some new tools, like Cursor AI, have changed the way we code. Cursor is an AI-driven editor where you can talk to the code using chat and natural language. Other tools, like Windsurf (Codeium IDE), combine a full code editor with AI “agents” that remember context and can fix errors in your project.
GitHub Copilot and CodeWhisperer are gaining attention as AI tools that can help with coding. Many teams are interested in these tools because they can improve code writing and make development faster.
How Developers Use AI Tools
In daily work, developers weave AI tools into their workflow. Here are some common practices:
Pair programming with AI:
Just like a human partner, a developer leaves comments or asks the AI to write or explain code. For example, a programmer might type ‘//’, write a function to sort this array, and then the AI provides the function. They work together to improve it.
Context-aware searches:
Instead of searching on Google, developers use AI chat to ask about bugs or APIs. The assistant understands the project’s code and provides tailored answers. This helps save hours of looking through generic documentation.
Automating small PRs:
Routine fixes, like formatting, renaming variables, or small bug patches, are done using AI-generated pull requests. The team then reviews these suggestions and merges them if they are good. Over time, this process can greatly reduce the code review workload.
Learning and onboarding:
Junior engineers often use AI to learn quickly. They can ask questions in simple language about the code or best practices and receive immediate answers.
Developers understand that tools like Copilot and Cursor are not perfect. Many check the output carefully. A study showed that experienced coders actually took 19% longer to complete tasks when using these tools. This delay likely happens because they need to review and fix the code suggested by the AI. This shows that human oversight is still very important. As Jellyfish points out, teams should “measure the real impact” of AI tools before using them widely.
When used wisely, AI tools can change how developers spend their time. GitHub says teams that use Copilot spend less time on routine tasks and more time solving creative problems. Many developers also want to use AI for writing tests and documentation in the future. In short, AI coding tools act like another team member: they handle simple tasks, allowing human developers to focus on design, architecture, and innovation.
Conclusion
AI coding tools are changing how software development works. By using AI in code editors and workflows, teams can code faster, make fewer mistakes, and handle routine tasks in no time. These tools can assist at every stage, from writing and improving code to creating tests and documentation. When used properly, they can increase productivity—over 80% of developers have reported this—and allow engineers to focus on bigger challenges. While these tools are helpful, they aren’t perfect, and expert review is still necessary.
As an IBM guide points out, AI is changing the roles of engineers and how they work, enhancing human skills. In summary, AI tools for coding help developers create better software more quickly.
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