Top 17 Generative AI-based Programming Tools (For Developers)
Hold on to your keyboards, developers! The age of generative AI has arrived, and it’s changing the game. With over 100 million users, ChatGPT is just one example of how generative AI is transforming the way we write code. These tools can analyze patterns in existing code and generate new lines of code that are optimized for readability, efficiency, and error-free execution. They can even assist with code review, identifying potential issues and helping teams maintain high-quality codebases. While they’re not perfect yet, AI-based programming tools are improving rapidly and have the potential to revolutionize software development by reducing barriers to entry and boosting productivity. So if you’re ready to collaborate with AI and take your coding skills to the next level, check out this in-depth review of the top 17 generative AI-based programming tools.
Introduction
We’ve entered the age of widespread adoption and democratized access to generative AI products. The use cases are everywhere — from generating images to writing code — and adoption has outpaced every consumer tech trend of the past decade. In Image Generation, MidJourney has grown to ~13 million members in its Discord, while ChatGPT reportedly reached over 100 million users. Use cases for software developers are also exploding — as of September, over 1.2 million developers had used GitHub Copilot’s technical preview. ChatGPT has also proven surprisingly adept at coding applications — from generating full code from text prompts (albeit often with many bugs) to bug-fixing code.
We expect the capabilities of large language models for coding to increase together with adoption going forward as models and training sets get more tailored to programming tasks. There are signs OpenAI is focussing on this potential with reports that they have hired ~400 developers as contractors to help label code for training the next generation of their models. While we don’t expect these products to replace software developers anytime soon — we do expect them to provide significant productivity enhancements and allow developers to focus on more difficult problems.
The progress so far suggests generative AI models are likely to become an essential tool for developers with their ability to write, debug, and optimize code. They have already begun to transform the way code is written, reviewed, and improved. With advanced algorithms, these models can analyze patterns in existing code and generate new lines of code optimized for readability, efficiency, and error-free execution. This can save developers time and also improve the quality of the code produced. Generative AI can also assist in code review by automatically identifying potential issues and improvements, security vulnerabilities, alternative approaches to improving code performance, and helping teams to maintain high-quality codebases. By automating several tedious and repetitive coding tasks, these tools have the potential to boost productivity.
In their current state, AI-based programming tools are not flawless but are improving rapidly. While the productivity boost is for big and small businesses alike, it is a game changer, in particular for small businesses, independent developers, and professionals. These tools could be particularly impactful and beneficial in reducing the barriers and costs of entry to new startup businesses.
The future of software development can see a collaboration between humans and AI, where the AI acts as a mentor, guiding developers through the code-writing process and ensuring that they are on the right track. With the ability to learn and adapt, the potential of generative AI in coding is exciting and limitless.
The article provides an in-depth review of the current AI-powered programming tools designed for code completion, generation, debugging, and performance improvement. The tools are categorized as popular, upcoming, or new, enabling users to select the best fit based on their needs, budget, and project complexity.
The Most Popular Tools: ChatGPT and Copilot
According to research conducted by GitHub, surveying over 2000 developers, they found that GitHub Copilot helped developers to stay in the flow (73%) and preserve mental effort during repetitive tasks (87%). AI-powered tools help developers write code faster by cutting down on repetitive tasks, maintaining productivity, and leveraging context by analyzing millions of programming codes in different languages. Simply put, AI-powered programming tools such as ChatGPT and CoPilot reduce the number of keystrokes.
1. ChatGPT (Popular)
ChatGPT, an advanced natural language processing model from OpenAI, has taken the world by storm. ChatGPT is fine-tuned from a model in the GPT-3.5 series. It’s a Transformer neural network with 175B parameters. It assists developers with coding by providing simple text prompts that leverage context, code templates, and programming knowledge to help with code completion, generation, optimization, documentation, performance, correction, and explanation. Using its advanced capabilities, ChatGPT can analyze source code and offer insights into coding languages, solves coding problems, and advice on software development.
ChatGPT can assist developers in writing unit tests by analyzing the code and suggesting test cases based on understanding the code’s behavior and functionality. This can significantly reduce the time and effort required for writing unit tests and improve their accuracy. Additionally, ChatGPT can provide feedback and recommendations to improve code quality and help developers better understand errors or enhancements.
While ChatGPT is a useful tool for various programming tasks, it cannot replace developers. It cannot be used to learn how to code, and it is not a replacement for good coding practices. Instead, it is a supplement to enhance productivity and output. ChatGPT is a powerful tool that can assist developers.
Pros:
ChatGPT works as a mentor for code completion, optimization, performance, correction, and explanation.
It can provide insights into the design and structure of the code.
It can help identify areas that may need refactoring, simplification, review, or improvement.
It can generate source code documentation by analyzing the code and providing explanations and annotations.
Cons:
ChatGPT lacks an inherent understanding of code but recognizes correct code structure.
Rephrasing the prompt can lead to varying outcomes.
The training data is restricted to 2021, limiting its scope.
The tool may not be the ideal choice for generating code that requires extensive context.
Category: Code Generation, Code Completion, Code Optimization
Price: Free or $20/mo
2. Copilot (Popular)
Copilot is a Transformer model with 12B parameters.
GitHub Copilot is an AI-powered pair programming editor designed to assist developers in working faster and more efficiently. It uses OpenAI Codex to convert natural language into coding prompts as developers write comments or lines of code. When developers write comments that describe their desired logic, GitHub Copilot can generate a code suggestion for their solution automatically. Moreover, it can adapt to the developer’s coding style by adjusting to their edits. In essence, it serves as a smart autocomplete tool for developers.
This code completion solution is compatible with a vast array of programming languages and frameworks, including Python, Java, JavaScript, TypeScript, Ruby, and Go. It can be used as an extension for popular code editors, such as Visual Studio Code, Neovim, and JetBrains. The extension is available on desktop and can also be utilized on cloud-based solutions, such as GitHub Codespaces.
GitHub Copilot’s prompt engineering pipeline is quite sophisticated. It suggests which variable to fill in based on the context in which you are writing the code.
GitHub Copilot has its strengths and weaknesses. While it provides features like smarter code completion and contextualized solutions, which reduce the amount of time spent searching for solutions, the suggested code is only a suggestion. It is up to the developer to assess these suggestions and decide whether to accept, skip, or ignore them.
Pros:
Copilot is compatible with multiple programming languages
It has access to a vast dataset of public code repositories on GitHub
It utilizes contextualized solutions to write code
It is excellent at writing boilerplate code.
Cons:
There are issues with code maintainability, configuration, compatibility, and versioning.
The current version of CoPilot cannot remove, simplify, or refactor code.
The tool is not ideal for novice developers.
CoPilot only offers security suggestions if prompted.
Category: Code Generation, Code Completion
Price: Free or $44/user/mo (Team Plan)
Top AI-powered Programming Tools for Developers
1. Replit Ghostwriter (Popular)
Replit Ghostwriter is an ML-powered tool that provides code completion, generation, transformation, and in-editor search capabilities. It consists of four primary components: Complete Code, which analyzes your code and offers suggestions for continuations; Generate Code, which produces new code based on your suggestions; Transform Code, which assists you in refactoring or modernizing code to meet standards; and Explain Code, which analyzes existing code and explains its function using natural language, as well as Semantic Search. Ghostwriter is designed to complement your existing programming knowledge and reduce the time you spend searching for help or studying code examples on sites like Stack Overflow.
Pros:
Ghostwriter helps reduce friction by using code and comment context.
It can refactor your code to run faster and translate it into another language.
It is a web browser application; users don’t need to download anything.
It auto-saves generated code onto Cloud and needs no deployment process.
Cons:
It provides limited storage, CPU, and RAM.
Category: Code Generation, Code Completion, Code Refactoring, Code Explanation, Tests Generator
Price: Free or $7/mo
2. TabNine (Popular)
TabNine is an AI code completion tool that uses deep learning algorithms for intelligent code completion in languages such as Java, Python, and C++. It automatically indexes your code and creates customized suggestions based on your writing patterns. Its advanced AI capabilities offer features, such as automated code completion, auto-generated tests, syntax highlighting, and integration with popular IDEs. TabNine supports over 20 languages and 15 editors, including VS Code, IntelliJ, Android Studio, and Vim. Although it is not an end-to-end code generator, it enhances an IDE’s auto-completion capability. TabNine also offers a cloud-based version that gives developers access to their coding tools from any device.
Pros:
TabNine boosts the development pace with real-time code completion.
It protects code privacy.
It offers individualized language models that collaborate after thoroughly evaluating models of various sizes.
It provides a private model trained on your code.
Its predictions are both locally and cloud-based.
Cons:
Its free version can only really provide suggestions for small projects.
The model uses a lot of memory for a text editor feature on top of the text editor you’re using.
The tool’s UX can get busy with irrelevant suggestions.
Category: Code Generation, Code Completion, Cloud, Local, Fine-tuning
Price: Free or $12/mo
3. OpenAI Codex (Popular)
Codex is a natural language processing model, based on OpenAI GPT-3, that can generate working code in over a dozen programming languages, including Python, JavaScript, Go, Perl, PHP, Ruby, Swift and TypeScript, and even Shell. The data used to train Codex includes billions of lines of source code from publicly available sources, as well as natural language, including code from public GitHub repositories. It has a memory of 14KB for the Python code. Codex is a powerful, transformer-driven system. It understands your task and fulfills it most effectively and efficiently.
Pros:
Codex can access various APIs and manipulate data.
It can describe existing code.
It automatically generates documentation for an existing codebase of a company or open-source project.
The model can self-reference, store previous commands in memory within the same session, and use them accordingly.
Users can view the code generated by the tool from the given prompt, enabling them to understand what the tool has interpreted.
Codex provides an extended response that includes additional information relevant to the initial request.
Cons:
It may create duplicate functions.
It is difficult to fine-tune the generated code.
It has a restricted setup that makes it difficult to include functions and extensions.
Category: Code Generation, Code Completion, Code Refactoring, Code Explanation, Tests Generator
Price: Free (Limited Beta)
4. CodeWP (Popular)
CodeWP is an AI WordPress code generator built by Isotropic. It is specifically trained and optimized for WordPress website creators. It supports JS and PHP, as well as modes specific to popular plugins like WooCommerce and major page builders. CodeWP includes features such as live collaboration, real-time code feedback, and a wide range of plugins for different programming languages. Additionally, it integrates with GitHub, enabling easy version control and collaboration on projects. CodeWP is a valuable tool for teams seeking an easy-to-use and collaborative code editor.
Pros:
CodeWP supports 11+ programming languages.
It’s AI model is trained specifically for WordPress.
It is a multilingual tool.
Cons:
It can be lightly buggy & slow at times.
It is not suitable for complex projects or large-scale applications.
Category: Code Generation, Code Explanation, Domain Specific
Price: Free or $12/mo
5. MutableAI (Upcoming)
MutableAI is an AI-powered coding assistant that helps automate repetitive coding tasks and speeds up production with natural language input. One of its most exciting features is the open-ended query dialogue, which allows users to ask complex questions. MutableAI offers domain-specific transformations that understand your code seamlessly, making it easier to accomplish tasks efficiently. Other features include auto-completion, open-ended transformations, the ability to productionize code, and type annotations. Overall, MutableAI is a powerful tool that can help developers save time and increase productivity.
Pros:
MutableAI offers domain-specific transforms that understand your code seamlessly.
It is trained on a more curated set of high-quality code.
It offers real-time pairing and the ability to share coding sessions.
Cons:
There are slight issues with stability and reliability.
The tool has a steeper learning curve, especially for its advanced features.
Category: Code Generation, Code Completion, Code Refactoring, Code Explanation
Price: Free or $10/mo
6. CodeSquire (Upcoming)
CodeSquire is an AI-powered code-writing assistant that is specifically designed for data scientists, engineers, and analysts. It provides intelligent code suggestions, assists with data exploration, and automates repetitive tasks. It allows developers to translate comments into code, creates entire functions without searching for library methods and parameters, write SQL queries with text prompts, and generate code explanation for someone else’s code. Currently, CodeSquire works as a browser extension on Google Colab, BigQuery, and JupyterLab.
Pros:
CodeSquire is tailored to the needs of data scientists.
It offers proper notebook support, data awareness, and cell output.
Cons:
It is not as effective for complex programming tasks.
It is available on limited platforms.
Category: Code Generation, Code Completion, Code Refactoring, Code Explanation, Domain Specific
Price: Unavailable (Freemium/Paid)
7. Salesforce CodeGen (Upcoming)
Salesforce CodeGen is an open-source model that facilitates program synthesis, enabling conversational AI programming. It is trained on a vast corpus of natural and programming languages, using a 16-billion parameter auto-regressive language model. CodeGen goes beyond code autocompletion and seeks to understand the user’s ultimate goals, empowering them to develop apps more quickly and with less coding. This opens up more time for complex tasks that benefit from a human touch.
Pros:
Salesforce CodeGen’s features eliminate the constraints by generating executable code.
Its application is simpler than writing prompts–it is simply talking.
Cons:
It could have limited community support and resources available.
Category: Code Generation, Open Source
Price: Free
8. Figstack (New)
Figstack is a web-based platform that assists developers in comprehending any code in any language, translating programming languages, and automating documentation for functions. It integrates with popular code editors like VS Code, enabling developers to access its features while working on their projects. Figstack provides features like autocomplete, code snippets, and real-time debugging, allowing developers to write code more efficiently and with fewer errors. Furthermore, Figstack offers a robust answering platform that enables developers to search for code examples and solutions to common programming problems, reducing the time spent searching for answers.
Pros:
Figstack helps in measuring the efficiency of codes in Big O notation.
Its no-code UI makes the tool accessible to developers of all skill levels.
Cons:
It may not be suitable for more complex projects that require advanced customization options.
It is a cloud-based tool, so there may be concerns about data privacy and security.
Category: Code Explanation, Code Translation, Automated Documentation
Price: Contact Vendor for Pricing
9. DataMaker (New)
The Datamaker Coder Tool simplifies the coding process for Webflow by using natural language processing to generate custom code snippets for HTML, CSS, and JavaScript that can be used in projects. The tool also provides the ability to manipulate collections, apply functions on certain breakpoints, and add additional functionality to code. Additionally, DataMaker supports a wide range of programming languages, including Python, Java, JavaScript, C, C++, C#, Go, Rust, Ruby, Swift, and HTML/CSS. However, it’s important to note that while Datamaker can offer many benefits to developers, it’s important to evaluate whether it’s the right fit for a specific use case before using it.
Pros:
DataMaker has been designed to learn from its mistakes and improve over time.
It is simple and intuitive to use for novice developers.
Cons:
It may be difficult to navigate or configure the tool for some users.
Achieving specific results may require experimentation.
Users do not have ownership of the generated code.
Category: Code Generation, Domain Specific
Price: $9.99/mo
10. AirOps (New)
AirOps is a cloud-based platform that simplifies application deployment and management for developers. It can be accessed through a Chrome extension, web app, or API, making it easy to integrate into any workflow. Its standout feature is the SQL assistant, which provides developers with tools to write, optimize, update, fix, and explain queries. AirOps enables developers to easily analyze their databases, identify and fix performance bottlenecks, and automate repetitive tasks. While AirOps offers many benefits, some developers may prefer alternative tools for managing their applications or writing SQL queries.
Pros:
AirOps provides AI-powered recipes for SQL writing, fixing, and explaining.
It can auto-document table schemas.
It allows users to generate Python scripts and dbt YAML files.
Cons:
It may be less suitable for smaller development teams because of its complexity and cost.
As a cloud-based platform, any issues with the platform’s stability or downtime could significantly impact the development process.
The platform may not be customizable enough to meet the unique needs of some development projects.
Category: Code Generation, Code Refactoring, Code Explanation, Cloud, Domain Specific
Price: $159/mo
11. What The Diff (New)
What-the-Diff is an AI-powered app that reviews the diff in pull requests and writes a descriptive comment about the changes in plain English. It uses the GitHub API to get the pull request diff and then employs an AI model to generate a description of the changes without storing the code. What-the-Diff understands the context of the changes and provides insight into what and why the changes were made. One unique feature is its ability to highlight semantic differences besides the usual line-by-line code comparisons, allowing developers to quickly and accurately identify issues. Another useful feature is the ability to identify and ignore certain differences that are not relevant to the code changes, such as differences in white space or formatting. However, as a relatively new tool, What-the-Diff may not yet have all the features and integrations that more established comparison tools offer.
Pros:
What the Diff supports almost all programming languages.
It features a clean user interface.
It provides complete control over when and how to analyze your pull requests.
Cons:
It requires full access to your code to read the diff.
It may not be suitable for larger codebases or complex changes.
While the tool identifies differences between code changes, it may not offer solutions or fixes for the identified differences.
Category: Code Explanation, Code Refactoring
Price: Free or $19/mo
12. AI Query (New)
AI Query is a powerful natural language processing tool that enables developers to interact with their databases using plain English sentences, which it then translates into SQL queries. This tool offers a unique feature by being able to understand complex queries and generate SQL queries that can be executed on the underlying database. It is highly versatile, capable of handling multiple databases and different database schemas, and supports a wide range of data types. With AI Query, developers can generate SQL queries easily and quickly, and it offers a range of features, including SQL Query Generation, SQL Explanations, and Database Schema.
Pros:
AI Query allows users to save and share SQL queries.
It supports PostgreSQL, MySQL, MariaDB, and SQL Server.
The tool stores SQL query history.
It is user-friendly and can be used by individuals with no coding background.
Cons:
It may not always understand the query intent, leading to incorrect translations or data retrieval.
It may not support all SQL syntax, limiting the queries that can be executed.
It needs manual query adjustments for complex queries and the underlying data structure.
There is a potential lack of transparency in the inner workings of the AI algorithm.
Category: Code Generation, Code Explanation, Domain Specific
Price: $10/mo
13. Adrenaline (New)
Adrenaline is a software debugging assistant that uses machine learning to help developers identify and fix bugs in their code more efficiently. It automates the process of generating hypotheses about what could be causing the bug. It also provides real-time feedback on the developer’s actions to help them test and refine those hypotheses. Adrenaline uses a combination of program analysis, statistical reasoning, and probabilistic inference to identify the most likely cause of the problem.
Pros:
Adrenaline leads to faster and more accurate bug identification, as well as a reduction in the time and resources required for manual debugging.
It displays the fixes in-line like a diff
It explains errors in plain English using GPT-3
Cons:
It needs the OpenAI API key of users.
There is a possibility of incorrect diagnoses if the input data is not properly preprocessed.
Category: Debugging Assistant, Open Source
Price: Free or $5/mo
14. Adept (New)
ACT-1 by Adept is an AI-powered code completion tool that uses deep learning algorithms to provide intelligent code suggestions and complete code blocks in real-time. Its large-scale Transformer model, ACT-1, has been trained to utilize digital tools, including web browsers. Currently, it is integrated with a Chrome extension that allows it to observe browser activities and perform various actions such as typing, clicking, and scrolling. Moreover, the model can handle tasks that involve combining multiple tools since most computer tasks require the use of multiple programs. In the future, ACT-1 is expected to ask for clarifications about what the user wants, making it even more helpful. While it may not know everything, ACT-1 is highly coachable and can correct mistakes with a single piece of human feedback, becoming more useful with each interaction.
Category: Process Automation
Price: Yet to release.
15. AlphaCode (Popular)
Transformer-based language model. 41.4 billion parameters.
AlphaCode, developed by DeepMind and Google, is a powerful tool for generating competitive programming solutions. It is highly specialized, having been exclusively trained on how people answered questions from software writing competitions. AlphaCode exhibits a unique skill set that combines natural language understanding, problem-solving abilities, and the statistical power characteristic of large language models. It has successfully solved the Backspace problem and shows significant improvement over previous AI coding systems that relied on explicit instruction. AlphaCode models were pre-trained on 700GB of GitHub open-source code to learn code representations and solve explicit coding tasks.
Category: Code Generation, Competitive Coding
Price: Yet to release.
Conclusion
Generative AI is transforming the way code is generated, enabling coding automation to a large extent. Its ability to automate tasks has enhanced productivity and efficiency in programming. While AI-powered coding is a significant leap toward the future, the current tools are still evolving. These tools are great supplements for coding practices, but they are not perfect. The key elements are supervision and partnership between AI and humans. As these models work towards improving quality and accuracy, it is imperative to understand the importance of human expertise and supervision to make these tools efficient coding partners.
This article provides an assorted list of tools for novice developers, advanced projects, and everything in between. Without the ability to guide AI in the right direction or the ability to cross-check what AI has produced, these tools can be counter-productive. The tools may generate perfect code or an irrelevant output — making it essential for developers to distinguish between good and bad code to use these tools effectively.
Source: https://pub.towardsai.net/top-17-generative-ai-based-programming-tools-for-developers-652977ac47b4
