Code Faster and Smarter with AI: Top Recommended Dev Tools

Introduction: AI is Revolutionizing Software Development in Powerful Ways

Artificial intelligence tools are rapidly transforming how developers code by automating repetitive coding tasks and significantly boosting productivity. The latest AI coding assistants are capable of far more than just suggesting small code completions. They can generate entire functions and classes, detect bugs, optimize code bases, and much more.

In this post, we'll explore some of the top new AI-powered developer tools based on their capabilities and positive reviews from software engineers. Leading companies like TabNine, GitHub, DeepCode, and LogDNA are creating AI-first coding tools that help developers write higher quality code faster, enable better collaboration across teams, and build world-class applications.

AI coding tools have the potential to automate up to 50% of repetitive coding work for backend, frontend, mobile, DevOps, and data engineers. This allows developers to spend time on more strategic tasks like innovating, collaborating, and planning. Specifically, AI can assist with:

• Automatically generating boilerplate code like database models in Python and TypeScript interface definitions
• Providing intelligent code completions and function suggestions in real-time as developers type
• Detecting potential bugs, performance issues, and security flaws by analyzing codebases
• Optimizing code through automated refactoring and restructuring
• Grouping and prioritizing logging errors and exceptions to simplify debugging
• Acting as an AI pair programmer to boost productivity and code quality
• Speeding up development cycles to allow more time for innovation
• Improving team collaboration for complex codebases and cross-functional features

Code Generation with AI

One of the biggest pain points for developers is writing repetitive boilerplate code like CRUD database models or repetitive API endpoint definitions. New AI code generation tools like TabNine and GitHub's Copilot aim to eliminate much of this grunt work.

These tools use advanced machine learning models trained on millions of public code repositories to predict and suggest likely code completions in real-time as developers type in the editor. For example, after a developer defines a Python class, GitHub Copilot might suggest an entire init constructor method based on common patterns.

Rather than just autocompleting small code blocks, GitHub Copilot is capable of generating entire functions, classes, interfaces, and libraries after "learning" the context from the developer's codebase. One user reported Copilot generating a fully functional React login component after just typing const Login = () => {.

Early feedback from developers highlights huge time savings - up to 50% in some cases - when using AI code generation tools for common backend, frontend, mobile, DevOps, and data engineering tasks. Codex, OpenAI's powerful natural language model that powers Copilot, supports advanced code generation in Python, JavaScript, TypeScript, Ruby, Go, Java, C#, and more.

As developers continue to use these models, they can provide feedback to further "train" and customize suggestions for their specific codebase conventions and patterns. Over time, the tools become more intelligent and the code suggestions get better and better.

TabNine

• TabNine’s local AI assistant integrates seamlessly into popular code editors like VSCode to provide intelligent code completions as you type
• Uses advanced machine learning models trained on millions of GitHub repositories
• Supports JavaScript, Python, Java, C++, C#, PHP and other languages
• Available for free with “Pro” version for teams with advanced capabilities
• Developers report huge productivity gains and fewer bugs after integrating TabNine

GitHub Copilot

• Copilot suggests entire lines and blocks of code inside the Visual Studio Code editor
• Powered by OpenAI Codex, a state-of-the-art AI system trained on public code
• Supports Python, JavaScript, TypeScript, Ruby, and more languages on roadmap
• Technical preview available currently with paid plans launching later
• Early reviews praise massive time savings writing code across many languages

AI-Powered Code Reviews and Refactoring

In addition to writing new code, developers spend countless hours reviewing code, identifying issues, and improving code health through refactoring. New AI services like DeepCode AI and Code Climate can automate much of this work to boost engineering productivity.

These tools analyze codebases using advanced ML models to detect potential bugs, security vulnerabilities, performance bottlenecks, and code smells. The models "learn" common bug patterns and problematic coding practices to identify areas for improvement.

For example, DeepCode AI specializes in identifying null pointer exceptions, SQL injections, unused variables, excessive complexity, and unoptimized algorithms in Java, JavaScript, C#, Python, and 10+ other languages.

Code Climate's maintainability checks and duplication detection works well for improving large Ruby on Rails and Python codebases. AI-assisted code review surfaces issues like these before they can reach production.

Beyond identifying problems, AI services also help developers refactor and restructure code for improved design, efficiency, and maintainability. They can optimize imports, fix naming inconsistencies, standardize code style, reduce complexity, and suggest design pattern improvements. Automating these repetitive but critical tasks enables developers to focus their energy on building product features rather than technical debt.

Regularly applying AI code reviews and refactorings results in cleaner, more resilient codebases less prone to bugs and outages. Developer satisfaction also improves when they can focus on creative problem solving versus manual code audits and rote refactoring.

DeepCode AI

• Scans code and automatically detects critical bugs, performance issues, security flaws
• Supports 30+ analyzers optimized for Java, C/C++, C#, JavaScript, Python, TypeScript, and more
• Identifies duplicate code snippets and suggests simplifications
• Integrates directly into developer workflows including GitHub repos and IDEs
• Strengthens code quality before changes get merged into the main codebase

Code Climate

• Analyzes large codebases for maintainability, test coverage, duplications, and other issues
• Provides issue priority scoring based on technical debt, security, performance impacts
• Supports 17 languages including Ruby, JavaScript, Python, PHP, and more
• Integrates with GitHub, CI/CD pipelines, Slack, and other essential dev tools
• Essential for monitoring and incrementally improving long-term codebase health

Simplified Debugging and Root Cause Analysis

Debugging errors and exceptions in complex applications can eat up huge amounts of developer time. AI-powered log analysis tools like LogDNA help developers quickly diagnose tricky bugs by automatically grouping similar stack traces and highlighting the most problematic errors.

Once errors have been identified, tools like Sentry use machine learning to triage issues by criticality. This allows high severity bugs to be prioritized and escalated faster. AI assistance also provides root cause analysis by connecting related events across the full stack request flow.

Instead of developers spending hours trying to parse through log files and stack traces, these tools analyze all of the data in real-time using natural language processing and clustering algorithms. This augmented intelligence saves significant time debugging issues and minimizing application downtime.

LogDNA

• Ingests, analyzes, and correlates any application or infrastructure log data
• Leverages AI techniques to parse unstructured log data and highlight key insights
• Auto-grouping similar stack traces surfaces the core issues instantly
• Integrates with Slack, PagerDuty, Datadog, and 100+ other tools
• Essential for monitoring and troubleshooting cloud-native applications

Sentry

• Provides full-stack error monitoring with advanced analytics and ML-powered grouping
• Triages issues by criticality and identifies root causes using event correlation
• Supports all major languages and frameworks with extensive customization
• Integrates with GitHub, Slack, Jira, and other dev tools via robust API
• Enables teams to identify and fix issues much faster

Stay Focused with AI Pair Programmers

In addition to generating code and identifying issues, AI can also help developers collaborate. AI pair programming tools like TabNine Assistant and GitHub Copilot provide context-aware suggestions and augmented code reviews in real-time as developers write code.

Developers can have natural language conversations with these tools to get personalized recommendations and coding assistance without being interrupted or distracted. Rather than switching contexts, these AI assistants enable hyperfocus while providing a safety net that catches bugs and anti-patterns.

Having an "AI colleague" work alongside human developers boosts overall productivity while enhancing code quality. The tools act like a mentor or senior engineer providing advice but let developers retain control over all coding decisions and style preferences.

AI assistants reduce distractions, direct developers down productive paths, and result in more maintainable code. Developers at leading companies like Airbnb, Netflix, and Shopify have reported huge gains in focus and flow when paired with tools like TabNine Assistant and GitHub Copilot.

TabNine Assistant

• Local AI pair programmer integrated directly into code editor
• Proactively suggests code edits, completions, and refactors as you type
• Natural language search helps instantly find relevant functions
• Supports Python, JavaScript, Java, C++, C#, PHP, and more
• Helps developers stay focused and productive while coding

GitHub Copilot

• Your AI pair programmer inside the Visual Studio Code editor
• Generates entire code snippets and functions inline as you code
• Training on huge volumes of public code enables intelligent recommendations
• Technical preview available currently with paid plans launching later
• Early feedback suggests massive boosts in developer productivity

Conclusion and Key Takeaways

AI coding assistants like TabNine, GitHub Copilot, DeepCode, and LogDNA are transforming development workflows for the better. These tools intelligently generate code, optimize codebases, spot bugs, triage issues, and enable immersive coding with AI pair programmers.

Key takeaways:

• Leading AI coding tools integrate directly into developer workflows like GitHub and IDEs
• Automating repetitive tasks allows developers to focus on higher value work
• Code generation, code analysis, debugging, and collaboration see outsized benefits
• Natural language conversations create more intuitive interactions
• AI enables developers to code significantly faster while improving quality
• Developers report up to 50% time savings on common coding tasks

AI is rapidly revolutionizing software development. Developers building the future should seriously consider leveraging AI tools like those featured on DevHunt to code faster, debug easier, strengthen quality, and boost collaboration. The future of coding is AI-augmented.