As a software developer, you can deploy AI-driven tools to complete your code, test it for bugs and inconsistencies, and even converse with an unknown codebase. This is 2023, the year of artificial intelligence. You are missing out if you are not using AI code assistants and other AI-powered tools within your development environment.
There are already too many AI tools for developers to choose from. Here, we’ve hand-picked the best AI tools for a variety of purposes – code generation, testing, debugging, and documentation among other things.
List of the best AI tools for developers in different categories
AI code generation/completion tools
AI tools for documentation
AI tools for debugging and code optimization
AI-driven testing tools
Codebase analysis tools
AI-based tool for code migration
Meeting notes made easy with AI
Before we review the tools in further detail, let’s talk about strategies developers can implement to augment their development efforts with AI – saving time, automating repetitive tasks, and freeing up bandwidth to focus on pressing issues.
Strategies for software developers to use AI tools for coding
AI-augmented software development is part of the natural evolution of computer science. While human intelligence is still key to a number of aspects of good-quality coding, the incorporation of AI can take your productivity and accuracy to a whole new level.
Use generative AI tools to autocomplete your code
Integrated development environments have long been helping developers with suggestions based on their coding behavior, the language, and the codebase. AI takes it much further by understanding natural language descriptions of code tasks using advanced language models. So, now AI-powered code editors can
- Complete your code as you type
- Rectify typos and syntax errors
- Generate code based on a certain task and context.
AI for codebase analysis
When you start working on a project built by other developers, you could end up spending days to fully understand the codebase. Now, with AI tools like Adrenaline in the equation, you can converse with the codebase as if it were a person with in-depth knowledge of the code.
Take advantage of AI code review tools
Code reviews are tedious yet essential. With AI tools, you can automate the process of code reviews. Trained on large code repositories and millions of execution traces, these tools can help you find
- security vulnerabilities
- scopes for optimization
In fact, there are tools that can predict bugs in your code. And then there are AI code refactoring tools that can help you automate code refactoring.
Code migration with AI tools
Thanks to AI tools, code migration, and translation are almost instantaneous processes now. Developers can save hundreds of hours by deploying automated migration tools, hours that they’d otherwise lose converting millions of lines of legacy code into a modern, more efficient programming language.
Testing tools powered by AI
Testing a software product comes with many challenges the peaks of them being tight deadlines and wrong test estimations. These issues can be easily circumnavigated with the help of AI.
AI can help you generate and run tests, significantly increasing test coverage and reducing the possibility of introducing new bugs.
AI for developers but not for programming
There is more to software development than just writing code. As a software engineer, you need to communicate with clients, stakeholders, and business leaders to understand their specific requirements. Moreover, you need to create documentation for your code so that others can understand your strategies and pick up where you’ve left off without much hassle.
So, you need
AI tools for meeting notes
Tools that can transcribe and summarize meetings are for everyone but they can be especially useful for developers working in a large team where requests and requirements pour in from all directions.
AI tools for documentation
It’s hard to find developers who love to document their code. Then again, it’s essential not only for other developers to understand your code, but also for the original coder to look back and make sense of their own code. Now, you have tools that can generate documentation suitable for multiple purposes – education, user guidelines, and manuals.
11 AI tools every software developer should use in 2023
In this section, we will take a deeper look into ai tools for developers built for various purposes. We will start with the AI code completion tools and work our way through all the other categories.
GitHub Copilot can generate code suggestions from natural language prompts. It works with a dozen of different programming languages and integrates smoothly with your existing code editor. More than a million developers and 20,000 businesses have used GitHub Copilot generating 3 billion lines of code since its launch in October 2021
GitHub Copilot understands the style, context, and conventions of a project and shows suggestions that are consistent with the ongoing work. You can just cycle through the lines of code written by the copilot and select the snippets you want to use, reject, or edit.
- GitHub Copilot suggests code based on comments made in natural language. All you need to do is describe the logic you want.
- Suggestions are rooted in the context of the project
- Integrates with popular IDEs like Visual Studio, VS Code, Neovim, and Jet Brains.
- Helps you adapt to a new language or framework
- Suggests multi-line functions
- Speeds up test generation
- Filters out vulnerable coding patterns
- You can choose to block code suggestions that match public code (on GitHub).
Individuals can get GitHub Copilot for $100 per year. You get a free trial, of course. Businesses can get GitHub Copilot for $19/month per user. The business version comes with features like policy management, proxy support, and better privacy.
Tabnine uses deep learning algorithms to offer code completion based on the context, the development project, and your local code repository. Unlike GitHub Copilot which is an entirely cloud-based tool, Tabnine uses a local LLM to analyze your repo and incorporates the knowledge from it while making code suggestions.
- Full function completion based on the function declaration.
- Generates code blocks from natural language prompts.
- It can be adapted to your localized codebase without breaching privacy.
- Avoids copyleft exposure. (Makes use of open-source code/libraries in a way that the developers’ intent is served without legal complications)
- You can run Tabnine on-premise, on a secure SaaS, or on your virtual private cloud
Tabnine has a free version that offers short code completion based on permissive open-source code. The natural language to code completion feature comes with the Pro plan which costs $12 per month per user. You get context-aware code completion based on your own repo with the enterprise plan.
Mintlify has two products. One is Mintlify Writer which generates documentation for your code using AI. The documentation is created in the form of comments.
The other product, which is simply called Mintlify, connects code with documentation that sits elsewhere. It recognizes when enough code is changed to make the existing documentation incorrect.
Both tools address specific challenges related to documentation and both can save a boatload of time for developers.
- It allows you to change the style and structure of the content to match your brand’s standards
- Automatically adds meta tags to help with SEO
- Creates SEO-optimized sitemaps
- Easy to deploy – has a GitHub app too.
- Offers line-by-line suggestions to improve documentation using GPT4.
Mintlify has a free tier that comes with automated API documentation and SEO-optimized documentation. The startup plan comes for $120 per month with features like intelligent content suggestions, conversion insights and analytics.
Amazon CodeGuru is a review service by AWS that uses machine learning and program analysis to find defects in code that are extremely difficult or time-consuming for developers to detect.
It proactively identifies shortcomings in the code to help developers address them early. It also offers recommendations for better programming practices and a more maintainable code base.
- Offers recommendations pertaining to complex issues like resource leak prevention and security analysis
- Works with Java and Python repositories in GitHub, Amazon CodeCommit, Amazon S3, and BitBucket among other source providers.
- It helps you find the most expensive lines of code by analyzing runtime behavior
Amazon CodeGuru has a free tier. The standard paid plan costs $10 per month for 100k lines of code. They charge $30 more for every next 100k lines.
Code Defect AI is a part of Microsoft’s AI Lab Projects. It is a machine learning classifier that “predicts source code files carrying a higher risk of a bug.”
Code Defect AI trains custom classifiers on the metadata associated with historical commits in GitHub projects. The classification algorithm is designed to recognize certain patterns in the software project’s code base that might introduce bugs. The goal is to detect the bugs before the code is deployed.
- Early detection of potential bugs
- The predictions are explained using Local Interpretable Model-Agnostic
- Developers can learn from the predictions
Testim is an AI-driven testing tool for web-based user interfaces as well as mobile apps. It uses the power of AI to author tests faster while also increasing their stability in the face of UI changes. The tool comes with a host of features that target software-testing pain points. Overall it brings purpose-built capabilities for generating tests, analyzing test failures, agile testing, and maintaining tests.
- Smart user flow recording
- Auto-capture elements
- Smart locators that identify changes in the application and keep the test stable
- Visual review of the test execution
Testim has a free tier that allows one user per organization. The essentials and the pro plans cost $450/month and $1000/month respectively.
Diffblue Cover uses generative AI to autonomously write Java unit tests (JUnit or TestNG). It also updates your unit test library automatically when code changes are made.
- Ready to use unit tests that can run, compile, and accurately validate code behavior
- Autonomous maintenance of tests
- Insightful reports and intelligent recommendations
- Automatically makes changes to your code to make it more efficient and testable
Adrenaline is a code analyzer paired with an AI chatbot by OpenAI Codex. The product is as simple as it gets. Here’s what it says when you’re about to get started.
“I’m here to help you understand your codebase. Get started by importing a GitHub repository or a code snippet. You can ask me to explain how something works, where something is implemented, or even how to debug an error.
That is all there is to this tool. No elaborate marketing statements, no fluff. You show it some code, it will analyze it. Then you ask a question, and it’ll answer it.
Cody by Sourcegraph is another tool that is adept at understanding your code base. This one is not as barebones as Adrenaline although they share most features. Cody can explain a code base, work as a pair programmer to help you detect code smells, and summarize changes made to the code base, among other things.
- Translates code snippets from one language to another
- It can help you locate functions and components within the codebase
- It will detect issues like unhandled edge cases, unclear variable names, and magic numbers.
The above features aside, Cody can also write unit tests, offer code completion, and generate documentation. You can try Cody Beta for free.
Grit is a great tool for fixing technical debt. It combines the powers of a proprietary query language GritQL and AI transformers to automate the repetitive task of modernizing outdated code.
Grit is currently in private Beta. You can apply for early access or ask for a demo. Nevertheless, looks like the tool will really blow up when it hits the market. Code migration is a serious issue for developers. Grit is promising to make it super easy allowing developers to focus on the more important stuff.
Software development is more than just writing code. It’s also about effective communication. That is where Otter.ai comes into play with its automated meeting note generation. It automatically joins your meetings, records the audio, and transcribes it accurately. It even creates real-time summaries so that someone can just jump in and catch up without asking anything.
- Accurate transcriptions and summaries
- Real-time meeting summary
- Incorporation of slides in the transcripts
- Chat with the transcript to get instant answers instead of reading through it
Otter has a free variant that offers almost all important features along with 300 monthly transcriptions. The business plan costs $20 per user per month.
The relevance of human developers in the presence of generative AI tools
Software developers build applications that will eventually be used by human beings (at least that’s the intention in most cases). It takes a human being to understand another. It is still difficult for AI to imagine every aspect of the user experience or to conjure up features out of the blue to bring novelty to a product. That’s why we have AI assistants to augment our work and not AI developers to steal it.
Here’s what we bring to the table
- Innovative thinking to find novel solutions to complex problems
- Domain expertise to add nuance to a product built for a specific industry
- Ethical decision-making to ensure that a piece of software doesn’t breach boundaries
- Complex problem-solving that goes beyond AI capabilities.
As a developer or an aspiring developer in 2023, it is very important that you adapt to the presence of generative Ai and make the best use of the best AI coding tools not only to ease up your work but also to learn and do more in shorter spans of time.