Essential AI Tools to Boost Developer Productivity and Security

Code smarter! Explore top AI assistants for faster & safer development

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With the rapid advancement of technology, AI developer tools have become indispensable. These tools streamline the coding process and contribute significantly to the efficiency and innovation in software development.

AI developer tools enable developers to automate repetitive tasks, optimize performance, and uncover novel solutions. As a result, programmers can focus more on creative aspects, problem-solving, and the strategic design of applications, fostering a dynamic and adaptive approach to software development.

Five types of AI developer tools

AI developer tools target different parts of the development lifecycle and collectively contribute to a more streamlined and effective development pipeline. Terminology in AI can get confusing, so make sure to check out our AI Glossary.

1. Coding assistants

Coding assistants are typically implemented as plugins within IDEs and collaborate seamlessly with developers by offering autocomplete suggestions and supporting essential functions during the coding process. These tools enhance the coding experience by assisting in code completion, reducing syntax errors, and promoting efficient coding practices within the development environment. They are pivotal in improving developer productivity and code quality by providing real-time assistance throughout the workflow.

While AI coding assistants are valuable tools for developers, enhancing productivity and efficiency, they are designed to build code and address code functionality, not security. Therefore, pairing them with security tools like Snyk is crucial to prevent the accidental introduction of vulnerabilities into the code.

2. Chatbots and LLMs

Chatbots and general large language models (LLMs) are either code-specific, like OpenAI code, or are helpful in planning and discussing code. These tools are dynamic in that they support collaborative efforts in conceptualizing, refining, and communicating coding concepts. Their adaptability makes them valuable assets for various stages of the development process. 

3. Security companions

Security companions, often implemented as plugins, provide code security analysis during development, some even in real-time. These tools go beyond conventional static analysis and actively scan and identify potential vulnerabilities, with some fast enough to do so as AI writes or generates code. By seamlessly integrating into the development workflow, security companions contribute to creating more robust and secure code, fostering a proactive approach to addressing potential vulnerabilities before they become critical issues.

4. AI code search

AI-powered search tools leverage advanced algorithms to quickly locate specific sections within a codebase. These tools use AI to understand and interpret code semantics, which allows developers to perform rapid and accurate searches. By harnessing natural language processing and machine learning capabilities, AI search tools improve the precision of code retrieval, making it easier for developers to navigate complex projects. These tools help accelerate the development workflow, ultimately contributing to increased productivity in development.

5. AI code testing

 AI code-testing tools autonomously generate test cases and scenarios for code evaluation. By analyzing the codebase, understanding its logic, and predicting potential use cases, AI code-testing tools contribute to the automation of the testing process. Integrating AI in code testing improves efficiency and promotes more robust and reliable software by addressing potential vulnerabilities and ensuring the stability of the codebase.

AI developer tools help to improve efficiency, security, collaboration, and the overall quality of software projects. 

Top 8 AI developer tools

Today, AI developer tools are becoming essential for streamlining and enhancing the development lifecycle; here are the top eight available right now:

1. Snyk Code

Snyk Code is an AI-backed solution that identifies issues within developer-created and AI-generated code, including tools like GitHub Copilot or Amazon CodeWhisperer. It assesses and fixes source code rapidly in real-time, using the full context of applications in its scans, and directly providing fast and early feedback within integrated development environments (IDEs). Snyk Code is also unaffiliated to, but integrates with, any AI coding assistants. This ensures impartial code reviews, and maintains a system of checks and balances for good governance.

  • Type of tool: security companion

Advantages: Snyk Code automates the identification and fixing of vulnerabilities in both developer-created and AI-generated code, offering a more comprehensive and efficient approach to code security.

2. GitHub Copilot

GitHub Copilot stands out as one of the most widely used coding assistants, seamlessly integrating into major IDEs and offering robust support. Its affordability makes it a popular choice among developers.

  • Type of tool: coding assistant

  • Advantages: Enhances speed, reduces errors, and provides more efficiency and accuracy in coding compared to manual methods.

3. Amazon CodeWhisperer

Like GitHub Copilot, Amazon CodeWhisperer is meant for applications and environments utilizing AWS services. It serves as a valuable coding assistant within AWS-centric development workflows.

  • Type of tool: coding assistant

  • Advantages: The tool offers real-time assistance, helping developers write code more efficiently by providing context-aware suggestions and automating specific coding tasks. This efficiency can lead to faster development cycles compared to manual coding.

4. ChatGPT / GPT-4 (API)

As a driving force in generative AI, ChatGPT, and its successor GPT-4 (API), offers broad utility, ranging from code generation and code functionality reviews to content creation like blogs, poems, stories, and email authoring.

  • Type of tool: chatbot / general LLM

  • Advantages: ChatGPT/GPT-4 automates and diversifies content creation, making it faster and more versatile than traditional manual writing.

5. Claude.AI

Claude.AI, an AI Chatbot from Anthropic, is similar to ChatGPT but boasts a large context window. It excels in summarizing extensive documents, accepting attachments, longer blocks of text, and even code.

  • Type of tool: chatbot / general LLM

  • Advantages: Claude.AI's larger context window allows for a more comprehensive understanding of complex documents, making it advantageous for handling extensive text and code, though it doesn’t have any specific security expertise or benefits.

6. Sourcegraph

Sourcegraph's Cody is an AI assistant within IDEs that comprehensively understands and explains code, identifies code smells, and auto-generates code and unit tests for functionality.

  • Type of tools: coding assistant

  • Advantages: The enhanced precision of code discovery and understanding provides more efficient and accurate support than traditional methods.

7. Codium

Codium is an AI code-testing tool that builds tests for code in real-time as developers write it, instilling confidence in code quality within the IDE before committing changes.

  • Type of tool: AI code-testing

  • Advantages: A more immediate and integrated testing approach than traditional manual testing.

8. Phind

Phind is an AI-backed search engine that allows you to ask questions about your code, describe issues you're having to get code examples and assist in building your applications; it is available online and in your IDE.

  • Type of tool: AI search

  • Advantages: A more effective and context-aware code search compared to traditional search engines, providing relevant code examples and assistance for application building.

Next steps with AI + Snyk

Snyk, a developer security platform, provides developers with more sophisticated code security analysis capabilities that traditional methods struggle to provide because we combine the power of a state-of-the-art hybrid AI with deep human security expertise. 

Snyk's use of AI is a cybersecurity approach driven by the need for more advanced, efficient, and scalable code analysis methods, vulnerability detection, and overall risk management in cybersecurity's dynamic and complex landscape.

With the Snyk platform, you have robust cybersecurity functionalities like:

AI-powered code security fixes

Finds, and fixes complex issues in IDEs while protecting integrity and code ownership.

AI-assisted security rules

Reviews all the security rules Snyk Code uses when scanning source code for vulnerabilities.

AI-powered code-checker 

Checks code (including AI-generated code) security before the next PR commit and sends alerts of critical bugs. (You can try the free online code checker, powered by Snyk Code, now!)

Open source dependencies security

Scans and monitors open source components, providing vulnerability detection and remediation guidance.

Open source package recommendations 

Helps find the best packages for open source projects.

Security-trained AI engine 

Provides one-click security fixes and comprehensive app coverage, helping developers build fast and stay secure by finding and fixing vulnerabilities and managing tech debt.

Are you ready to learn more about how Snyk uses AI to improve cybersecurity posture? Book a live demo with a security expert today!


Securing the software supply chain with AI

Discover how AI is both a threat and a solution for securing software supply chains. Learn about emerging AI attack vectors, AI-powered defenses, AIBOMs, and how Snyk can help.

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