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AI Tools for Developers: How to Speed Up Your Workflow
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5 min read
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AI tools are revolutionizing software development, with recent studies showing they can help engineers code 35-45% faster and document 50% faster. From code generation to automated testing, these tools are empowering developers to focus on more creative and challenging aspects of their work while streamlining repetitive tasks.

AI Tools Overview

The tech landscape changes fast, and developers are constantly looking for new ways to speed up their workflow. Artificial Intelligence is taking off on this front at remarkable speed, offering solutions that automate many mundane tasks, improve coding, and even predict bugs before they manifest
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No matter if you are an established software engineer or a beginner developer, using AI tools can help you code better, smarter, and not harder
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In this article, we've collected some of the best AI-powered tools for developers to help speed up their development workflow
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1. AI-Powered Code Generation

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simplilearn.com
Much of the initial work involved in software development merely involves producing boilerplate code. Writing these workarounds is tedious, which is why a growing number of coders are welcoming AI-powered code generation tools, which use machine learning models to predict lines of code that might be helpful based on your input.
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GitHub Copilot, a plug-in from the founders of OpenAI with suggestions based on the company's Codex model, lives in your IDE (integrated development environment) so that, as you type, it suggests lines of code, predicts what function you might be writing, and even recommends best practices for what you're trying to accomplish. It automates repetitive tasks and lets you spend more time on innovating and solving problems.
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Tabnine is another tool that can be quite useful. It directly inserts itself into your IDE and predicts and completes lines of code. It's accessible through more than 20 programming languages and learns from your codebase, which helps speed up tasks and improve code quality.
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2. AI for Code Debugging and Error Detection

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Chasing and repairing bugs can be a huge time-suck for any developer, but AI tools are becoming increasingly useful for predicting them shortly after they are written.
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DeepCode is a machine learning-powered code review tool to analyze your code for potential bugs, security holes, and poor performance based on its training on vast amounts of open source code and examples of known issues. It can tell you how to avoid disaster before the next Cheeseburger.
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Where others tools might concentrate on the quality of code, Snyk looks specifically at security. It is an AI-powered code file scanner that looks across your codebase, dependencies, containers, and infrastructure for code for vulnerabilities that can create security risks for your application.
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3. AI for Code Refactoring and Optimization

Refactoring code to improve its structure, readability, or performance is another area where AI is excelling. AI-driven code refactoring tools can take a look at your code and decide whether and how to rewrite it in a way that would improve the performance and maintainability of the code.
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CodeAI is software that uses AI to help developers improve their code and identify elements that could hinder performance, by automatically refactoring and optimizing their creations.
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Also, the company JetBrains' Resharper is famous for its refactoring power, and with post-2021 AI integrations, can now suggest intelligent refactorings, provide automatic code style improvements, and ensure conformity to coding standards.
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Thanks to their capabilities, AI tools can help you shave hours – sometimes even days – of manual labor off some of the more onerous aspects of code refactoring.
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4. Automating Testing with AI

Testing is one of the most critical steps in the development lifecycle, and AI is increasingly aiding to reduce the time spent on it. From unit testing to performance testing, AI helps to generate test cases and predict possible bugs. They can even simulate an environment to check the quality of a piece of code.
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Testim leverages machine learning to make writing and maintenance of tests easier. It learns from how your application changes and automatically updates the test cases for you, avoiding the constant need for manual updates to them. Through its API, Testim integrates cleanly into your CI/CD pipeline and, for example, lets you run tests as part of your build.
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Applitools offers 'visual' testing, which checks that your app's user interface works on all browsers and screen sizes. It uses AI to hide potential UI bugs. If the UI changes (say, between browsers), Applitools will recognize that there was a change that could be a bug.
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These tools greatly relieve developers from mundane and time-consuming test generation and maintenance tasks, enabling them to concentrate on development areas that are much more important. AI-based testing tools can hasten developer release cycles and guarantee a higher degree of stability.
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5. AI-Driven Documentation and Learning

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AI tools can also assist in writing and testing code, but also in documenting that code, and learning from existing codebases. Documentation is an often-overlooked resource in software projects that can make all the difference in maintaining and scaling a project. A growing number of tools can help generate and maintain documentation, to save time and maintain consistency
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Mintlify is an 'AI documentation tool' that automatically creates docs for your codebase. It analyzes the functions, variables, and classes in your code and creates human-sounding documentation 'with context, descriptions and comments'
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CodeGuru assists developers in writing better code and also helps them learn by explaining why performance is slow and describing the recommended improvements or best practices. It's essentially an AI-infused code reviewer and a profiler, identifying the issue, making suggestions for fixing the issue, and explaining why the prescribed fixes should be made
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AI tools such as Mintlify and Amazon's CodeGuru can automatically provide documentation and deep learning insights, helping developers maintain high-quality code while making it much easier to learn from their mistakes
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6. AI-Enhanced Collaboration and Project Management

Development teams need to work together, and there are AI tools that improve how teams communicate, track their project's status, and stay updated. AI-powered project management tools track patterns in the workflow and aid teams in improving workflow efficiency by tuning task distribution, scheduling, and performance.
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For example, Jira's AI features can help teams automate tasks, estimate time to completion, and optimize sprint planning. By leveraging historical performance data, a didactic program can anticipate bottlenecks, suggest assignment priorities, and predict delivery dates.
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Trello's Butler AI helps automate repetitive tasks such as moving cards, setting due dates, and setting priorities that fit into custom rules. The goal is to enable teams to spend less time fiddling with running the project and more time developing.
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Because of advances in AI-powered collaboration tools, logging information, and task allocation takes less effort and gets out of the way of coding and innovation.
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Last Thoughts on AI Tools for Developers

The above AI tools (and many others popping up in the market) are starting to automate a lot of time-consuming tasks while working together, and they're getting much better at detecting bugs without being directly told how to do it. By using AI tools from GitHub Copilot, Tabnine, DeepCode, and Testim, you'll likely be far more productive and better able to focus on writing the code instead of spending time figuring out how to do it.
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A growing number of developers have already come to rely on these tools. The faster and more efficiently you're able to code, the more competitive you'll be in a world increasingly dominated by AI. If you're a novice coder, using AI tools for writing could put you on the same level as a veteran developer. If you're a veteran coder, AI tools could help you code even smarter, even faster, and even better than you already do.
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Related
How does GitHub Copilot compare to Tabnine in terms of code accuracy
What are the limitations of using AI tools like GitHub Copilot and Tabnine
How do AI tools like GitHub Copilot and Tabnine ensure code security
Can AI tools like GitHub Copilot and Tabnine integrate with all programming languages
How do AI tools like GitHub Copilot and Tabnine handle code debugging
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