The code editors or software in the development process integrate AI tools. This automates complex steps in the development process. Developers get real-time feedback on code quality through these AI tools. We have listed the best AI tools in 2023 based on their usability. AI tools every developer must use to stay ahead of the competition.
TabNine
With AI-driven code suggestions, TabNine is a hit in the developer's community. It leverages machine learning algorithms to offer context-aware predictions. Based on the developer's pattern of writing code, it suggests code completions. Some features of TabNine include:
It integrates with Integrated Development Environments (IDEs) like Visual Studio Code, IntelliJ, Sublime Text, and Atom.
It supports over 20+ programming languages, including C/C++, TypeScript, React, and more.
It Converts natural language description into functional code.
TabNine provides privacy and security features to your code.
It gives quality output in any development field, whether web, mobile, or data science.
You can connect your codes to repositories such as GitHub, GitLab, and more.
The AI tool suggests automatic code refactoring to maintain code consistency and reduce review iterations.
GitHub Copilot
GitHub and OpenAI collaborated to develop GitHub Copilot. This AI-powered coding tool auto-generates and auto-completes code snippets. Copilot uses OpenAI’s advanced GPT model to provide context-based predictions. Some features of GitHub Copilot include:
It generates code segments based on the description, patterns, and contexts.
It integrates with the preferred coding tools like IDEs or visual studio code.
The tool can test your code, select code to perform different actions, and review existing code.
It supports various programming languages, including Python, TypeScript, Ruby, and more.
Copilot allows developers to share their code in real time with other developers.
Developers can track the progress of projects, including code suggestions.
The integration with GitHub’s code editor speeds up the coding process.
Amazon CodeGuru
AI-driven code review tool developed and managed by Amazon Web Services(AWS). With the automated code review functionality, Amazon CodeGuru reviews pull requests in corresponding repositories. This allows developers to deliver quality software solutions and enhance resource efficiency. Some key features of CodeGuru include:
It utilizes machine learning algorithms to identify potential issues and suggest recommendations for code optimization.
Reviews code to identify bugs and security vulnerabilities.
It helps developers to speed up the development process by reducing manual code review and optimizing performance.
It integrates with the preferred coding tools like IDEs through plugins and extensions.
CodeGuru provides real-time feedback and code suggestions to improve code quality.
The performance profiling feature helps developers to find and fix performance issues.
DeepCode
This AI-driven code review tool identifies potential coding errors and suggests improvements. Using DeepCode, developers tend to write cleaner code and improve coding standards. Some key features include:
It leverages machine learning to analyze code, spot bugs, and clean them up.
DeepCode supports almost all programming languages and is quick at identifying errors.
It is an independent platform or can integrate with the preferred code editors.
Developers can share, review and receive feedback on their code among the team.
IntelliCode
IntelliCode is an AI-powered coding tool by Visual Studio. Developers prefer it due to its context-specific code suggestions. During coding, IntelliCode identifies common coding tasks and suggests efficient action. Some key features of IntelliCode include:
It is a cloud-based tool supporting various programming languages, including Python, Kotlin, Ruby, Swift and more.
Through extensions and plugins, it can support additional programming languages.
IntelliCode leverages machine learning algorithms to provide code suggestions related to contexts.
Developers can share code across multiple contributors to get valuable recommendations.
Keploy
Keploy is an open-source, end-to-end (E2E) testing toolkit for developers. It creates test cases and data mocks/stubs by recording API calls, database queries, etc., making releases faster and more reliable.
Keploy works by being added as a middleware to your application. It captures and replays all network interaction served to the application from any source. This allows Keploy to generate test cases for all of your API endpoints, including those that are not explicitly tested by your unit tests. This can help you to identify and fix bugs that would otherwise go undetected.
Keploy can create data mocks/stubs for your APIs, which can help you to isolate your tests and make them more reliable. It can automatically compare test cases generated from previously collected traffic against updated behaviour of your application, and bring any differences to your attention. This can help you to identify regressions in your production code early on.
Conclusion
With AI-powered tools, developers are coding smarter, not harder. Not only do developers save time and efficiency, but they also improve the quality and maintainability of software applications. Developers can unlock potential coding abilities and ensure quality code with increased efficiency.
From code completion, suggestion, and bug detection to code review assistance and automated testing, we have suggested the AI tools every developer must use.
In this blog post, we have covered the best AI tools of 2023 that assist developers throughout the development process. As AI advances, it is expected to change the viewpoint of the development process with its robust tools.
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