AI coding tools are software solutions that use artificial intelligence to enhance various aspects of software development, including coding, debugging, and testing. They use AI algorithms to understand and predict a developer’s intentions, automating tedious tasks and allowing developers to focus on the more creative aspects of programming.
These tools leverage advancements in machine learning and artificial intelligence, notably large language models (LLMs), streamlining the coding process and making it more efficient. They have transformed software development by making coding more accessible, reducing errors, and improving code quality.
By analyzing vast amounts of code, AI coding tools provide recommendations, write chunks of code, and help with debugging and refactoring. They can also help write tests and create code documentation. This can significantly speed up the development process and enhance productivity.
Technologies used in AI coding
AI coding tools typically use one of the following technologies.
Natural language processing (NLP)
NLP allows computers to understand and process human language. It enables AI tools to interpret programmers’ instructions written in plain language and convert them into code. NLP also powers the generation of documentation and code comments, helping maintain code quality and ease of understanding.
Advancements in NLP have led to improved error detection and correction capabilities within AI coding tools. By understanding the context and semantics of code, these tools can identify and suggest fixes for syntactical and logical errors, making programming less frustrating for developers and reducing debugging time.
Deep learning
Deep learning, a subset of machine learning, is inspired by the workings of the human brain. It uses a large network of artificial neurons to process data, detect patterns, and make decisions and predictions. In AI coding tools, deep learning algorithms analyze vast datasets of code to identify patterns, generate meaningful suggestions, and better understand a developer’s intentions. Deep learning is also the basis for LLMs, discussed below.
Large language models (LLMs)
LLMs, such as OpenAI GPT-4 and Google Gemini, have revolutionized AI coding tools by providing a huge knowledge base from which to draw coding insights. These models have been trained on various programming languages and code examples, allowing them to understand context, generate human-like code and documentation, and offer suggestions that align with best practices.
Types of AI coding tools
AI coding tools can be classified into the following groups.
Code generation and completion tools
These tools predict what developers intend to write next, offering suggestions to complete lines of code or entire functions. This speeds up the coding process and helps in learning new programming languages by providing real-time examples and best practices.
Pair programming tools
These tools allow developers to collaborate with an AI partner, simulating the benefits of pair programming with a human. The AI provides suggestions, critiques, and writes code in real time, offering a collaborative coding experience. This AI assistance helps with code quality and education, both for junior developers and for experienced coders learning new languages or technologies.
These tools use AI to analyze code in real time, identifying errors, vulnerabilities, and inefficiencies. They go beyond traditional linting tools by understanding the context and semantics of code, making more accurate and helpful suggestions for improvements. These tools can pinpoint errors and suggest corrections or alternative approaches.
Code refactoring tools
These tools analyze existing codebases to suggest improvements, making code more efficient, readable, or maintainable. They identify redundant code, suggest design pattern implementations, and can even adapt code to newer programming practices. By automating the process of code optimization, these tools help maintain high code quality throughout the development lifecycle.
AI-powered code review tools automate the analysis of code changes and can review code quality, identifying potential issues before they’re merged into the main codebase. They examine code for adherence to coding standards and best practices, and can also help identify security vulnerabilities. They provide immediate feedback to developers, speeding up the review process.
Automated testing and debugging tools
These tools generate test cases, identify bugs, and suggest fixes. By understanding the intended functionality of software, they can create comprehensive tests that cover edge cases and unexpected inputs. They’re especially useful in generating unit tests and offloading tedious and repetitive work from development teams.
These tools automatically generate comments and documentation for codebases. They analyze the code to understand its functionality and purpose, generating relevant documentation that makes the code easier to understand and maintain. This can include automatic generation of code comments, tutorials, and pull request summaries.
AI coding tools are designed to enhance, not replace, the role of software developers. They automate mundane tasks, identify errors, and offer suggestions, freeing developers to focus on more complex and creative aspects of software development.
The human element — creativity, problem-solving, and understanding user needs — remains irreplaceable in developing meaningful and innovative software solutions. However, the emergence of AI tools encourages developers to adapt and learn new ways to interact with these technologies.
Embracing AI tools can lead to more rewarding and efficient software development careers where routine tasks are automated and human intellect is directed towards higher-level design and problem-solving.
AI-powered coding tools offer the following advantages:
Increased efficiency and productivity: By automating repetitive tasks, suggesting code, and identifying errors, developers can significantly accelerate the development process. According to research from McKinsey, AI can help developers complete tasks twice as fast as traditional methods.
Improved code quality: AI coding tools contribute to improved code quality by providing real-time suggestions, error detections, and refactoring options. They leverage vast datasets to suggest best practices and support adherence to coding standards.
Enhanced learning and skill development: Through real-time suggestions and examples, developers can learn new programming languages, frameworks, and best practices. AI coding tools provide a hands-on learning experience that reinforces knowledge through application.
Challenges of AI coding tools
While AI coding tools can be convenient and improve productivity, they also have limitations.
Dependence on data quality and availability
The efficacy of AI coding tools is heavily reliant on the quality and availability of data they’ve been trained on. Inaccuracies or biases in the training data can lead to incorrect suggestions, errors, suboptimal coding practices, or even security vulnerabilities. Ensuring the use of high-quality, diverse datasets is crucial for the performance and reliability of these tools.
Security and privacy issues
The integration of AI in coding tools raises concerns over security and privacy. These tools often require access to codebases and sensitive data, creating potential vulnerabilities. Ensuring the security of these tools and the data they process is paramount to prevent data breaches and maintain trust. Reliance on cloud-based AI services can compound these concerns.
Compliance and legal considerations
The utilization of AI coding tools also brings to the fore compliance and legal considerations, particularly concerning the training data used by these tools. AI models are trained on publicly available code repositories, which might be proprietary or provided under nonpermissive open source licenses. This raises the risk of infringing on intellectual property rights or using “copyleft” code that cannot be used in commercial software products.
8 best AI coding tools
1. Tabnineֿ
Tabnine is the AI code assistant that you control — helping development teams of every size use AI to accelerate and simplify the software development process without sacrificing privacy, security, or compliance. Tabnine boosts engineering velocity, code quality, and developer happiness by automating the coding workflow through AI tools customized to your team. It’s trusted by more than 1,000,000 developers across thousands of organizations.
Key features
Best-in-class AI code generation: Let Tabnine’s AI coding assistant streamline AI code generation and automate mundane tasks so you can spend more time on the work you love. Get accurate and personalized code completions. Add comments and other natural language prompts in-line or via chat and Tabnine will automatically convert them into code.
Supports all popular languages and IDEs: Tabnine supports more than 80 programming languages and frameworks such as Python, Java, Javascript, C, C++, Go, and more. Tabnine is easy to integrate with popular development environments, with plugins available for VS Code, the JetBrains family of IDEs (e.g., IntelliJ, Android Studio), Visual Studio, and Eclipse.
Protection from IP issues: Tabnine has trained its proprietary models (Tabnine Protected for Chat, and the universal model for code completion) exclusively on permissively licensed code. This ensures that the recommendations from Tabnine never match any proprietary code and removes any concerns around legal risks associated with accepting the code suggestions. Tabnine is transparent about the data used to train our proprietary model and shares it with customers under NDA. Additionally, Tabnine offers an IP indemnification to enterprise users for peace of mind.
Tabnine Chat: Tabnine includes Tabnine Chat, the enterprise-grade, code-centric chat application that allows developers to interact with AI models using natural language. It supports numerous use cases such as planning (i.e., asking general coding questions or better understanding code in an existing project), code generation, explaining code, creating tests, fixing code, creating documentation, and maintaining code.
AI personalized to you: In AI, context is everything. To increase the effectiveness of AI code assistants, it’s imperative to provide contextual awareness to the LLMs so that they can understand the subtle nuances that make a developer and organization unique.Tabnine leverages locally available data in the developer’s IDE to provide more accurate and relevant results.
This includes:
– Runtime errors
– Imported libraries
– Other open files
– Current files
– Compile / syntax errors
– Noncode sources of information
– Current selected code
– Connected repositories
– Conversation history
– Git history
– Project metadata and other project files
Personalized AI recommendations based on awareness of a developer’s IDE are accepted 40% more often than AI suggestions generated without these integrations. Developers can connect Tabnine to their organization code repos (e.g., GitHub, GitLab, Bitbucket) to gain global context. Tabnine also offers model customization — you can fine-tune Tabnine’s proprietary model using your own code to create a custom model. Model customization is extremely valuable when you have code in a bespoke programming language or a language that’s underrepresented in the training data set, such as System Verilog.
Switchable model selection: Access new state-of-the-art models in Tabnine Chat as soon as they become available. You can choose from Tabnine Protected, Tabnine + Mistral, GPT-4o, GPT-4 Turbo, GPT-3.5 Turbo, Codestral, Claude3, and Cohere’s Command R. You’re not locked into any one of these models and can switch instantly between models for specific projects, use cases, or to meet the requirements of specific teams.
Total deployment flexibility: Tabnine offers its customers numerous deployment options. Customers can consume Tabnine as a secure SaaS offering (in a multitenant environment or a single-tenant environment) or do a fully private installation (on-premises or on VPC) to ensure that their code stays in the boundaries of their corporate network and isn’t shared with any external party.
Enterprise-grade security: Tabnine offers key compliances like SOC 2 Type 2, GDPR, and ISO 9001 to ensure the security and privacy of your data.
Onboard onto projects in minutes: The Code Explorer agent for Tabnine helps developers ramp on a new project faster. For developers who are new to an organization or existing developers who are new to a project, Code Explorer provides a comprehensive overview of key project elements, including runnable scripts, dependencies, and overall structure to help them get up to speed effortlessly.
Plan your approach to development tasks: Ask Tabnine coding questions, learn how things work in your specific project, and get solutions and references relevant to your workspace. You can also use Tabnine to search your codebase. For example, if you were planning an approach to fixing errors in your log files, you can ask Tabnine to “find the errors in these log files,” and then prompt it to “generate and ASCII table showing the errors and their locations.” Then you could move on to fixing the errors by asking Tabnine to “provide solutions to fix and resolve these errors.”
Natural language code generation: Use natural language to generate code based on your design specs. Create software components, features, functionality, and more. As you continue coding, Tabnine will also provide in-line code completions, offering real-time, context-aware suggestions that seamlessly blend with your coding style. Tabnine can also support high-complexity tasks. For example, if you needed to create a function to parse an array and return specific values if criteria were found, you can use natural language to describe your requirements and prompt the AI agent to generate code matching those requirements. With Tabnine, you could also use “@” mentions to tag elements in the workspace to instruct the AI to generate code with specific context taken into account.
Unit test generation: Ask Tabnine to create tests for a specific function or code in your project, and get back the actual test cases, implementation, and assertion. Tabnine takes in the context from existing tests in your project and codebase to suggest tests that align with your project’s testing framework and variables.
Error fixing: Select or reference code with an error and Tabnine will recommend fixes. As your tools identify errors within your code and error notifications emerge in the problems tab or in-line using colored indicators, simply click on the error indicator and prompt Tabnine to suggest a fix. Rapidly accelerate error fixing without leaving your IDE and try multiple solutions to fix your errors. You can even use Tabnine to help resolve security issues identified by tools like Snyk.
AI documentation generation: Generate documentation for specific sections of your code to enhance readability and make it easy for other team members to understand. As you write code, use Tabnine to generate documentation including format documentation of classes and functions, comments, and in-line docs. Tabnine will generate standardized documentation, enhancing the readability of your code with every function, method, class, or line as needed clearly documented in a standardized format that’s easy to understand. If you want docs written in a specific format, you can even prompt Tabnine to do so. If you’re already using a documentation (format for example Google Java Style Guide), Tabnine will pick up on that and automatically generate documentation in your code that matches the context of existing documentation.
Code explanations: Tabnine Chat can provide you with an explanation for a block of existing code, which is especially useful when reading a new codebase or reading legacy code in languages you don’t know as well. This AI chat function allows for a pair programming experience, where the AI contributes to coding tasks, making it easier to work with unfamiliar codebases, frameworks, APIs, and languages.
Maintain and improve existing code: In addition to writing new code, Tabnine can help you change the existing code by adding functionality, refactoring, or fixing specific code with contextually relevant recommendations.
GitHub Copilot is designed to increase productivity by offering suggestions and automating parts of the coding process.
Key features
AI coding assistant: Functions as an AI assistant that provides real-time coding suggestions and converts natural language prompts into code.
Collaboration tool: Serves as a collaborative tool within development teams by offering fast answers to programming queries and suggestions for code improvement.
Pull request support: Assists in generating pull request descriptions and offers insights for reviewers to understand code changes better.
Limitations of GitHub Copilot (as reported by users on G2)
Understanding and relevance: GitHub Copilot may misinterpret the developer’s intent or the context of the code, leading to irrelevant or incorrect code suggestions.
Cost: Being a subscription-based service, the cost of GitHub Copilot might be prohibitive for some individuals or smaller organizations.
Security and code quality: GitHub Copilot might suggest code that introduces vulnerabilities or does not adhere to best practices, emphasizing the need for careful review of its suggestions.
Amazon Q Developer is Amazon Web Services’ offering for AI-driven code generation, providing real-time code recommendations as developers work. It’s based on a proprietary LLM that provides personalized coding suggestions.
Key features
Real-time code recommendations: As developers type code or comments, Amazon Q Developer offers suggestions tailored to the current coding context.
Code suggestions: Amazon Q Developer‘s suggestions range from single-line comments to entire functions.
Block completion: This feature supports the automatic completion of code blocks, such as if/for/while/try statements, based on the initial signature provided by the developer.
Function generation from comments: By interpreting comments within the code, Amazon Q Developer can suggest the signature of a function and its complete body, translating intent into executable code.
Limitations (as reported by users on G2)
Limited language support: Amazon Q Developer supports a narrower range of programming languages compared to its competitors, limiting its applicability across diverse development projects.
Limited IDE compatibility: Its integration is limited to VSCode, JetBrains, and select AWS IDEs, excluding widely used editors like Vim, Neovim, and Emacs.
Inconsistent code generation: The tool sometimes fails to generate useful code at expected points, produces errors like additional spaces, and suggests nonexistent methods.
Latency issues: The variability in latency, even for short suggestions, introduces uncertainty about whether a suggestion is being generated, impacting the coding workflow.
Codeium is another AI-powered code completion tool.
Key features
Speed and efficiency: Codeium offers autocomplete functionality that operates at low latencies, allowing developers to maintain their flow without interruption.
Wide language support: Codeium supports over 70 programming languages.
Codeium Chat: An AI-powered coding assistant within Codeium provides the ability to generate functions, explain code, refactor existing code, and translate code between languages.
AI suggestions: Codeium emphasizes the importance of developer oversight by allowing users to accept, reject, or modify AI-generated code.
Limitations of Codeium (as reported by users on G2)
Limited IDE integration: Codeium’s integration with IDEs is not seamless across the board. While it has a mature integration with Neovim and VS Code, other IDEs may not offer a smooth experience, with conflicts between Codeium’s suggestions and the IDE’s native Language Server Protocol (LSP).
Learning curve for beginners: The multiple suggestions offered by Codeium can be overwhelming for new users or junior developers.
Incorrect suggestions: Codeium sometimes offers incorrect suggestions, which can interrupt workflow and productivity and result in coding errors.
Sub-par language model: Users have noted that the tool’s underlying LLM technology is not at the same level as state-of-the-art tools, and often fails to generate code that accurately reflects the intentions of the user.
OpenAI’s text generation models, in particular the GPT series of LLMs, have helped advance the capability of machines to understand, generate, and interpret natural language, code, and images. These models can perform a variety of tasks, such as drafting documents, writing computer code, and providing technical tutoring.
Key features
Versatile applications: OpenAI is capable of drafting documents, coding, tutoring, translating languages, and more, adapting to a wide range of industries and tasks.
Ease of access: It’s accessible through the ChatGPT online interface and the OpenAI API, facilitating easy integration into existing systems and applications.
Structured output option: OpenAI offers the ability to return responses in JSON format, which is useful for tasks requiring structured data.
Reproducible outputs: OpenAI provides options for deterministic outputs, giving developers control over the consistency of responses.
Limitations of OpenAI (as reported by users on G2)
IDE support: Does not integrate into IDEs, limiting its usefulness for developers and forcing a context switch when using the chat interface.
Not specialized for coding: The GPT series of models was trained on a large corpus of text not limited to code. This makes them less suitable for many coding tasks compared to specialized tools.
Integration and dependency issues: The necessity of integrating with OpenAI’s external API can introduce dependencies that affect the reliability and performance of user-developed applications.
Rate limits: Encounters limitations due to API rate limits, which can restrict the volume of requests and consequently the tool’s utility in high-demand scenarios.
Data privacy concerns: Utilizing an external service raises concerns about data privacy and security, especially for applications dealing with sensitive or proprietary information.
Cody is an AI coding assistant designed to enhance the efficiency and understanding of coding practices for developers across various programming languages. It offers AI-assisted autocomplete features to speed up the coding process.
Key features
AI-assisted autocomplete: Offers autocomplete suggestions for single lines or entire functions across any programming language, configuration file, or documentation.
Cody Chat: An AI-powered chat feature that assists developers in navigating new projects, understanding legacy code, and tackling complex coding problems.
Integration with major IDEs: Cody is available for VS Code, JetBrains IDEs, and NeoVim.
Custom and prebuilt commands: Allows for the generation, testing, and fixing of code with one-click commands, including generating unit tests and optimizing code for best practices.
Limitations of Cody (reported by users on G2)
User experience challenges: Some users have found the user interface unintuitive, noting difficulties with tasks like excluding individual repositories.
Limited by interaction depth: There have been instances where Cody provides general advice instead of specific code examples, requiring further prompts from the user to obtain actionable code snippets.
IDE compatibility: While Cody supports major IDEs, its integration is labeled as experimental or in beta for some environments, which may affect stability and feature availability.
Dependency on Sourcegraph: Cody’s performance and capabilities are heavily reliant on integration with Sourcegraph’s tools, which might limit its use in environments where Sourcegraph is not deployed or available.
Replit AI provides an AI code generator embedded directly within a popular cloud-based IDE. It aims to enhance the productivity and creativity of developers by automating repetitive coding tasks, allowing them to focus more on the creative aspects of their projects.
Key features
Integrated AI chat: Replit AI incorporates a chat-based code generator within the IDE, enabling developers to interact with the AI without the need to switch between tabs.
Autocomplete code suggestions: The tool is designed to offer quick and unobtrusive in-line code suggestions.
Generative coding: With the ability to understand plain language prompts, Replit AI can generate and improve code examples, facilitating rapid development and iteration.
Code explanation: It offers clear summaries of code, making it easier to understand complex codebases or learn new programming concepts.
Limitations of Replit AI’s GhostWriter (as reported by users on G2)
Limited context awareness in some tools: The “generate,” “transform,” and “explain” functionalities seem to lack a comprehensive understanding of the project’s context, often providing generic solutions unrelated to the specific needs of the project.
Over-reliance on “chat” for context-sensitive suggestions: Users may rely almost exclusively on the “chat” feature for its better context awareness and cross-cutting suggestions. However, this requires cumbersome copying and pasting of code.
Lack of integrated change review: The absence of a feature to review and accept changes through a side-by-side diff make it harder to evaluate and incorporate AI-suggested modifications.
Absence of a “refactor” phase in development: The AI’s development process lacks a critical “refactor” step, which could have improved code quality and maintainability.
AskCodi is an AI-driven coding tool designed to simplify complex coding tasks. It aims to transform coding into a more manageable and efficient process.
Key features
Codi Chat: Enables developers to engage in code-related conversations, enhancing understanding and efficiency.
Workbooks: Jupyter-style notebooks that offer a flexible platform for coding, testing, and documentation are useful for both learning and development.
Codi integrations: Extensions for major IDEs, including Visual Studio Code, JetBrains, and Sublime Text.
Translate: An app that allows effortless translation between programming languages, facilitating cross-language development projects.
Quick suggestions: AI-driven code suggestions that speed up the coding process, reducing time spent on repetitive tasks.
Limitations of AskCodi (as reported by users on G2)
Limited IDE features beyond Chat: While the chatbot functionality is robust, AskCodi’s integration into IDEs could be expanded beyond simple chat interactions. There is a need for more interactive and integrated IDE features.
Code quality variability: The quality of code generated by AskCodi’s AI can vary, with some outputs not up to par with comparable tools.
Lack of real-time, context-aware recommendations: Developers could benefit from real-time suggestions that are aware of the current coding context. This feature would provide more accurate and useful assistance.
Separate interface for unit tests and documentation: A dedicated interface within the IDE for creating unit tests and documentation could streamline these tasks.
Conclusion
AI coding tools are reshaping the landscape of software development, ushering in a new era of efficiency and collaboration. By automating the more mundane aspects of coding, they allow developers to concentrate on the creative and complex challenges of building software.
These tools not only speed up the development process but also enhance code quality and facilitate the learning of new programming languages and practices. As the field of artificial intelligence continues to evolve, the integration of AI into coding tools is expected to grow, further revolutionizing the way developers work and create. This promises to make software development more accessible, faster, and of higher quality, benefiting both developers and the end-users of software products.
