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Published Oct 10, 2023 ⦁ 8 min read

Top 5 Dev AI Tools To Accelerate Delivery

Introduction

Artificial intelligence is transforming software development. AI-powered tools are emerging that aim to make developers more productive by automating repetitive coding tasks, generating boilerplate code, finding bugs, and even writing entire functions. The right developer AI assistant can help accelerate delivery of high-quality software.

This article covers the top 5 developer AI tools based on capabilities, user reviews, funding, and impact on the industry. We'll look at code autocompletion, automated code generation, identifying code issues, test case creation, and more. While AI is not a silver bullet, selectively applying these technologies alongside human oversight can boost output and free up developers for more strategic work.

First up - intelligent coding assistants.

AI Coding Assistants

AI coding assistants leverage models trained on millions of lines of code to provide context-aware suggestions as developers type. They can autocomplete boilerplate code, entire function definitions, import statements, and more. Top options include:

  • TabNine - Code autocompletions powered by a deep learning model. Can suggest relevant code snippets and function definitions tailored to your codebase.

  • Kite - Provides AI-powered code completions with documentation. Good for discovering new APIs and libraries.

  • GitHub Copilot - A code pilot from GitHub and OpenAI that suggests code snippets and even entire functions as you type. One of the most advanced AI assistants available.

  • Codex - Codex comes from OpenAI (the creators of GPT-3) and autocompletes code in over a dozen languages. Great for rapid prototyping.

  • REPL.it - This browser-based IDE offers AI-powered code completions including entire lines and functions. Handy for front end web development.

Key Benefits

  • Save time writing boilerplate code and documentation lookups
  • Reduce simple bugs by using reliable code snippets
  • Focus on complex program logic instead of mundane coding
  • Discover new APIs, libraries, and code techniques
  • Get examples and inspiration for implementing code

For example, Kite can provide instant documentation on new APIs as you code, ensuring you correctly implement them the first time. GitHub Copilot may suggest an entire function definition tailored to your application logic, saving you hours of work.

Limitations

  • May suggest incorrect or insecure code
  • Risk of overreliance - understand what's happening under the hood
  • Limited ability to explain the reasoning behind suggestions
  • Not a substitute for learning coding fundamentals and system design
  • BIAS issues if training data lacks diversity

For instance, GitHub Copilot may generate code that doesn't adhere to your team's security practices. Relying too heavily on autocomplete could also limit your understanding of the codebase over time.

Overall, AI coding assistants show promise in reducing repetitive coding tasks. But utilize them judiciously - don't become overreliant on the recommendations.

AI-Powered Code Generation

Moving beyond autocompletion, some emerging AI models can generate entire code snippets, classes, or functions based on natural language descriptions. Leading options include:

  • GitHub Copilot - Copilot can generate dozens of lines of code from comments and tests. One of the most capable AI code generators today.

  • Anthropic - Claude is an AI assistant by Anthropic that can generate full programs from descriptions. Still in private beta.

  • Runway ML - Focused on frontend development, Runway can generate React code from natural language and examples.

  • Pygmalion - A text-to-code generator that converts natural language and code snippets into full Python functions.

  • Aletheia - Aletheia by Nonah is an early-stage startup creating an AI assistant that generates code from descriptions.

Use Cases

  • Quickly create prototypes and proof-of-concepts
  • Bootstrap new projects and accelerate boilerplate coding
  • Automate coding for repetitive or straightforward tasks
  • Reduce time spent on mundane code
  • Allows developers to focus on complex program logic

For example, Pygmalion could rapidly generate a Python script to process and summarize a dataset based on a few descriptive comments. GitHub Copilot may create an entire CRUD API endpoint from scratch by analyzing test cases.

Challenges

  • Generated code often needs refactoring to meet project needs
  • Difficult to customize and tailor for specific use cases
  • May introduce new bugs or security flaws
  • Can obstruct developer's understanding of the codebase
  • Training data biases persist and require scrutiny

For instance, Copilot may generate verbose or inefficient code compared to a human. The code also requires careful review to avoid potential vulnerabilities from uncontrolled generation.

Automated code generation shows enormous potential but still has progress to make. Use judiciously for prototyping and simple tasks - and always thoroughly review the output.

Automated Code Review

AI-powered code review tools analyze source code to flag potential bugs, performance issues, security flaws, and code style violations. Top options:

  • DeepCode - Flags bugs, security vulnerabilities, performance issues, and more.

  • CodeQL - Creates a code graph to identify security risks like SQLi, XSS, and data leaks.

  • SonarQube - An open-source code analysis platform that identifies bugs, vulnerabilities, code smells, and more.

  • Codacy - Automated code reviews that monitor code quality and highlight issues to fix.

  • LogiGear TestArchitect - Automates test case design and does risk analysis with AI capabilities.

Advantages

  • Saves time spent on manual code reviews
  • Catches issues early, enabling rapid remediation
  • Enables proactive security practices within dev team
  • Provides consistent and objective analysis
  • Frees up developers to work on business logic

For example, DeepCode may catch input validation issues early, before they turn into costly security problems down the road. This allows developers to prioritize remediation.

Drawbacks

  • Prone to false positives - human oversight still essential
  • Cannot wholly replace nuanced human judgment
  • Limited ability to suggest ideal fixes tailored to codebase
  • May lack contextual understanding of code's end goals
  • Training data biases can propagate within AI models

SonarQube, for instance, often flags issues that turn out to be false positives after closer inspection. And the tools cannot weigh tradeoffs or make nuanced design decisions like an experienced engineer.

When used to complement human code reviews, AI-powered code analysis tools boost security and quality assurance. But some human oversight is still indispensable.

Automated Testing Tools

AI is also making inroads into software testing - automatically generating test cases, reducing flaky tests, and creating smart test suites. Leading options include:

  • Applitools - AI-powered visual testing and monitoring across desktop, web, and mobile apps.

  • Functionize - Smart test creation, maintenance, and analysis. Uses AI to generate tests.

  • Testim - Automates end-to-end testing with AI-generated tests that self-heal.

  • Mabl - Creates automated tests and maintains them through AI monitoring.

  • Autify - Automated UI test case generation for web apps. Records user interactions and generates reusable test scripts.

Pros

  • Decreased time spent manually coding tests
  • Achieves greater test coverage through automated generation
  • Frees developers from repetitive test creation and execution
  • Provides continuous feedback through AI monitoring
  • Reduces maintenance overhead through self-healing tests

For example, Testim can watch real user sessions and autogenerate robust end-to-end tests, even for complex workflows.

Cons

  • May generate ineffective or low-value test cases
  • Brittle tests prone to flakiness and breakages
  • Can miss critical edge cases lacking training data
  • Testing biases persist, requiring human oversight
  • Does not eliminate need for some manual testing

Functionize may create redundant or vaguely specified tests that provide little value. Edge cases not seen during training could be overlooked.

AI test automation shows promise but isn't a silver bullet. Use it to augment human-written tests, not replace them entirely. Monitor the test suites carefully and look for gaps or biases.

Conclusion and Key Takeaways

AI is transforming developer workflows - from assisting with coding tasks to reviewing code and generating tests. We covered the top tools across categories like intelligent coding assistants, code generation, automated code review, and automated testing.

Key benefits include improved productivity, higher quality code, accelerated testing, and more reliable applications. However, biases and limitations inherent in AI require vigilant human oversight and participation.

Used judiciously alongside engaged developers, AI tools can boost delivery velocity, free up developer time, improve code quality, and enhance application reliability. But they aren't a panacea - always apply critical thinking to take advantage of AI capabilities without introducing new risks.

The future looks bright for AI-assisted development that collaborates with engaged and empowered engineering teams. These top tools represent the vanguard of that future - with thoughtful application, they can accelerate delivery without sacrificing quality or control.

To learn more about leveraging AI and automation to empower your developers, explore the DevHunt platform. DevHunt is dedicated to showcasing cutting-edge developer tools to boost productivity. Check out featured AI assistants, code generators, testing tools, and more.

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