Best AI tools for< Improve Code Quality >

Swimm is an AI-powered code understanding tool designed to help developers work with and modernize legacy codebases efficiently. It provides contextual answers to complex coding questions, captures and utilizes developer knowledge, and offers static analysis of codebases to enhance documentation and code quality. Swimm integrates seamlessly into software development life cycles, enabling teams to preserve vital knowledge about their codebase and improve productivity. With features like tailored answers, code analysis, and developer knowledge capture, Swimm is a valuable tool for enhancing code understanding and collaboration.

Whybug is an AI tool designed to help developers debug their code by providing explanations for errors. It utilizes a large language model trained on data from StackExchange and other sources to predict the causes of errors and suggest fixes. Users can paste error messages and receive detailed explanations on how to resolve the issues. Whybug aims to streamline the debugging process and improve code quality by leveraging AI technology.

Tabnine is an AI code assistant that accelerates and simplifies software development while keeping your code private, secure, and compliant. It streamlines code generation, automates mundane tasks, and provides AI-powered chat support throughout the software development life cycle. Tabnine offers context-aware coding assistance tailored to individual users and teams, ensuring total code privacy with zero data retention. It also provides complete protection from intellectual property issues by relying on models trained only on permissive sources.

Magnet is an AI coding assistant that helps product teams fix issues, share AI threads, and organize projects. It integrates with Linear, GitHub, and Notion, and provides auto-suggested files and code files for personalized and accurate AI recommendations. Magnet also offers prompt templates to help users get started and suggests quick fixes for bugs or enhancements.

Metabob is an AI-powered code review tool that helps developers detect, explain, and fix coding problems. It utilizes proprietary graph neural networks to detect problems and LLMs to explain and resolve them, combining the best of both worlds. Metabob's AI is trained on millions of bug fixes performed by experienced developers, enabling it to detect complex problems that span across codebases and automatically generate fixes for them. It integrates with popular code hosting platforms such as GitHub, Bitbucket, Gitlab, and VS Code, and supports various programming languages including Python, Javascript, Typescript, Java, C++, and C.

Code Language Converter is an AI-powered tool that allows you to convert code from one programming language to another. Simply paste your code snippet into the converter and select the desired output language. The AI will then generate the converted code, which you can download or copy and paste into your project.Code Language Converter is a valuable tool for developers of all levels. It can save you time and effort by automating the code conversion process. Additionally, the converter can help you to learn new programming languages by providing you with a way to see how code is written in different languages.

Commenter.ai is an AI-powered tool that helps you write better comments on code. It can help you identify and fix common coding errors, suggest improvements to your code, and even generate new code for you. Commenter.ai is a great way to improve the quality of your code and make it more readable and maintainable.

What The Diff is an AI-powered code review assistant that helps you to write pull request descriptions, send out summarized notifications, and refactor minor issues during the review. It uses natural language processing to understand the changes in your code and generate clear and concise descriptions. What The Diff also provides rich summary notifications that are easy for non-technical stakeholders to understand, and it can generate beautiful changelogs that you can share with your team or the public.

Wand is an AI-powered tool that helps you find and fix errors in your code. It uses machine learning to identify potential problems and provides suggestions for how to resolve them. Wand can be used with a variety of programming languages, including Python, Java, JavaScript, and C++.

AI Code Reviewer is a tool that uses artificial intelligence to review code. It can help you find bugs, improve code quality, and enforce coding standards.

Snorkell.ai is an automated documentation generation tool that uses AI to create and update docstrings for GitHub projects. It supports multiple programming languages, including Python, JavaScript, TypeScript, Java, and Kotlin. Snorkell.ai integrates with GitHub and automatically generates docstrings whenever a pull request is merged, ensuring that documentation is always up-to-date with the codebase. It helps developers save time and effort by automating the documentation process, leading to improved code quality and reduced onboarding time.

Pixeebot is an automated product security engineer that helps developers fix vulnerabilities, harden code, squash bugs, and improve code quality. It integrates with your existing workflow and can be used locally via CLI or through the GitHub app. Pixeebot is powered by the open source Codemodder framework, which allows you to build your own custom codemods.

Kropply is an AI-powered debugging tool that helps developers fix logic, package, and unit-level bugs in their codebase once they run the code. It integrates with VSCode to provide real-time insights and error correction, streamlining the debugging process and making coding more efficient.

Korbit is an AI-powered code review tool that helps developers write better code, faster. It integrates directly into your GitHub PR workflow and provides instant feedback on your code, identifying issues and providing actionable recommendations. Korbit also provides valuable insights into code quality, project status, and developer performance, helping you to boost your productivity and elevate your code.

CodiumAI is an AI-powered tool that helps developers write better code by generating meaningful tests, finding edge cases and suspicious behaviors, and suggesting improvements. It integrates with popular IDEs and Git platforms, and supports a wide range of programming languages. CodiumAI is designed to help developers save time, improve code quality, and stay confident in their code.

Symflower is an AI-powered unit test generator for Java applications. It helps developers write and maintain test code with ease, saving time and improving code quality. Symflower works with JUnit 4 and JUnit 5 for Java, Spring, and Spring Boot applications.

PullRequest is an AI-powered code review as a service platform that offers on-demand code review from expert engineers enhanced by AI. It supports all languages and frameworks, helping development teams of any size ship better, more secure code faster through AI-assisted code reviews. PullRequest integrates with popular version control platforms like GitHub, GitLab, Bitbucket, and Azure DevOps, providing valuable knowledge sharing with senior engineers to improve code quality and security. The platform ensures code safety and security by adhering to best practices, strict procedures, and employing reviewers based in the US, the UK, or Canada.

MarsCode is an AI-powered platform designed to help developers code and innovate faster. It provides a collaborative environment with AI community docs to enhance productivity and creativity. With features like real-time code suggestions, automated debugging, and intelligent code completion, MarsCode empowers developers to streamline their workflow and build high-quality software efficiently.

CodeParrot is an AI tool designed to speed up frontend development tasks by generating production-ready frontend components from Figma design files using Large Language Models. It helps developers reduce UI development time, improve code quality, and focus on more creative tasks. CodeParrot offers customization options, support for frameworks like React, Vue, and Angular, and integrates seamlessly into various workflows, making it a must-have tool for developers looking to enhance their frontend development process.

Zevo.ai is an AI-powered code visualization tool designed to accelerate code comprehension, deployment, and observation. It offers dynamic code analysis, contextual code understanding, and automatic code mapping to help developers streamline shipping, refactoring, and onboarding processes for both legacy and existing applications. By leveraging AI models, Zevo.ai provides deeper insights into code, logs, and cloud infrastructure, enabling developers to gain a better understanding of their codebase.

Patched is an open-source workflow automation framework designed for development teams to build AI workflows that automate code reviews, documentation, and patches. It offers ready-to-go patchflows or the ability to create custom ones to accelerate mundane development tasks. Patched integrates seamlessly with popular platforms like Gitlab, GitHub, Jira, and more, allowing users to improve code quality, fix bugs, and create tickets efficiently. The application is privacy-focused, allowing users to deploy it within their own infrastructure for complete privacy. Patched is free and open-source, offering customization options via code or a no-code builder.

Sourcery is an automated code reviewer tool that provides instant feedback on pull requests, helping to speed up the code review process, improve code quality, and accelerate development velocity. It offers high-level feedback, line-by-line suggestions, and aims to mimic the type of code review one would expect from a colleague. Sourcery can also be used as an IDE coding assistant to understand existing code, add unit tests, optimize code, and improve code quality with instant suggestions. It is free for public repos/open source projects and offers a 14-day trial for private repos.

Momentum is an open-source behavioral auditor for backend code that helps developers generate powerful insights into their codebase. It analyzes code behavior, tests it at every git push, and ensures readiness for production. Momentum understands backend code, visualizes dependencies, identifies behaviors, generates test code, runs code in the local environment, and provides debugging solutions. It aims to improve code quality, streamline testing processes, and enhance developer productivity.

CodeSpin.AI is a set of open-source code generation tools that leverage large language models (LLMs) to automate coding tasks. With CodeSpin, you can generate code in various programming languages, including Python, JavaScript, Java, and C++, by providing natural language prompts. CodeSpin offers a range of features to enhance code generation, such as custom templates, inline prompting, and the ability to use ChatGPT as an alternative to API keys. Additionally, CodeSpin provides options for regenerating code, executing code in prompt files, and piping data into the LLM for processing. By utilizing CodeSpin, developers can save time and effort in coding tasks, improve code quality, and explore new possibilities in code generation.

AI Code Reviewer is a GitHub Action that utilizes OpenAI's GPT-4 API to provide intelligent feedback and suggestions on pull requests. It helps enhance code quality and streamline the code review process by offering insightful comments and filtering out specified files. The tool is easy to set up and integrate into GitHub workflows.

Analyze the following text from a github repository (name and readme text at end) . Then, generate a JSON object with the following keys and provide the corresponding information for each key, in lowercase letters: 'description' (detailed description of the repo, must be less than 400 words，Ensure that no line breaks and quotation marks.),'for_jobs' (List 5 jobs suitable for this tool,in lowercase letters), 'ai_keywords' (keywords of the tool,user may use those keyword to find the tool,in lowercase letters), 'for_tasks' (list of 5 specific tasks user can use this tool to do,in lowercase letters), 'answer' (in english languages)

Digma is a Continuous Feedback platform that provides code-level insights related to performance, errors, and usage during development. It empowers developers to own their code all the way to production, improving code quality and preventing critical issues. Digma integrates with OpenTelemetry traces and metrics to generate insights in the IDE, helping developers analyze code scalability, bottlenecks, errors, and usage patterns.

RTL-Coder is a tool designed to outperform GPT-3.5 in RTL code generation by providing a fully open-source dataset and a lightweight solution. It targets Verilog code generation and offers an automated flow to generate a large labeled dataset with over 27,000 diverse Verilog design problems and answers. The tool addresses the data availability challenge in IC design-related tasks and can be used for various applications beyond LLMs. The tool includes four RTL code generation models available on the HuggingFace platform, each with specific features and performance characteristics. Additionally, RTL-Coder introduces a new LLM training scheme based on code quality feedback to further enhance model performance and reduce GPU memory consumption.

Code Review GPT uses Large Language Models to review code in your CI/CD pipeline. It helps streamline the code review process by providing feedback on code that may have issues or areas for improvement. It should pick up on common issues such as exposed secrets, slow or inefficient code, and unreadable code. It can also be run locally in your command line to review staged files. Code Review GPT is in alpha and should be used for fun only. It may provide useful feedback but please check any suggestions thoroughly.

ICE-Score is a tool designed to instruct large language models to evaluate code. It provides a minimum viable product (MVP) for evaluating generated code snippets using inputs such as problem, output, task, aspect, and model. Users can also evaluate with reference code and enable zero-shot chain-of-thought evaluation. The tool is built on codegen-metrics and code-bert-score repositories and includes datasets like CoNaLa and HumanEval. ICE-Score has been accepted to EACL 2024.

Mastering GitHub Copilot for AI Paired Programming is a comprehensive course designed to equip you with the skills and knowledge necessary to harness the power of GitHub Copilot, an AI-driven coding assistant. Through a series of engaging lessons, you will learn how to seamlessly integrate GitHub Copilot into your workflow, leveraging its autocompletion, customizable features, and advanced programming techniques. This course is tailored to provide you with a deep understanding of AI-driven algorithms and best practices, enabling you to enhance code quality and accelerate your coding skills. By embracing the transformative power of AI paired programming, you will gain the tools and confidence needed to succeed in today's dynamic software development landscape.

Micro Agent is an AI tool designed to write and fix code for users by generating code that passes specified tests or matches design screenshots. It aims to streamline the code generation process by leveraging AI capabilities to iterate and improve code until desired outcomes are achieved. The tool focuses on test-driven development and provides interactive features for user feedback. Micro Agent is not intended to be a comprehensive development tool but rather a specialized agent for code generation and iteration.

The repository 'Awesome-LLM-Preference-Learning' is the official repository of a survey paper titled 'Towards a Unified View of Preference Learning for Large Language Models: A Survey'. It contains a curated list of papers related to preference learning for Large Language Models (LLMs). The repository covers various aspects of preference learning, including on-policy and off-policy methods, feedback mechanisms, reward models, algorithms, evaluation techniques, and more. The papers included in the repository explore different approaches to aligning LLMs with human preferences, improving mathematical reasoning in LLMs, enhancing code generation, and optimizing language model performance.

Code2Prompt is a powerful command-line tool that generates comprehensive prompts from codebases, designed to streamline interactions between developers and Large Language Models (LLMs) for code analysis, documentation, and improvement tasks. It bridges the gap between codebases and LLMs by converting projects into AI-friendly prompts, enabling users to leverage AI for various software development tasks. The tool offers features like holistic codebase representation, intelligent source tree generation, customizable prompt templates, smart token management, Gitignore integration, flexible file handling, clipboard-ready output, multiple output options, and enhanced code readability.

code2prompt is a command-line tool that converts your codebase into a single LLM prompt with a source tree, prompt templating, and token counting. It automates generating LLM prompts from codebases of any size, customizing prompt generation with Handlebars templates, respecting .gitignore, filtering and excluding files using glob patterns, displaying token count, including Git diff output, copying prompt to clipboard, saving prompt to an output file, excluding files and folders, adding line numbers to source code blocks, and more. It helps streamline the process of creating LLM prompts for code analysis, generation, and other tasks.

Awesome AI-Powered Developer Tools is a curated list of AI-powered developer tools that leverage AI to assist developers in tasks such as code completion, refactoring, debugging, documentation, and more. The repository includes a wide range of tools, from IDEs and Git clients to assistants, agents, app generators, UI generators, snippet generators, documentation tools, code generation tools, agent platforms, OpenAI plugins, search tools, and testing tools. These tools are designed to enhance developer productivity and streamline various development tasks by integrating AI capabilities.

avante.nvim is a Neovim plugin that emulates the behavior of the Cursor AI IDE, providing AI-driven code suggestions and enabling users to apply recommendations to their source files effortlessly. It offers AI-powered code assistance and one-click application of suggested changes, streamlining the editing process and saving time. The plugin is still in early development, with functionalities like setting API keys, querying AI about code, reviewing suggestions, and applying changes. Key bindings are available for various actions, and the roadmap includes enhancing AI interactions, stability improvements, and introducing new features for coding tasks.

An efficient implementation of a rate limiter for asyncio using the Leaky bucket algorithm, providing precise control over the rate a code section can be entered. It allows for limiting the number of concurrent entries within a specified time window, ensuring that a section of code is executed a maximum number of times in that period.

Skyvern automates browser-based workflows using LLMs and computer vision. It provides a simple API endpoint to fully automate manual workflows, replacing brittle or unreliable automation solutions. Traditional approaches to browser automations required writing custom scripts for websites, often relying on DOM parsing and XPath-based interactions which would break whenever the website layouts changed. Instead of only relying on code-defined XPath interactions, Skyvern adds computer vision and LLMs to the mix to parse items in the viewport in real-time, create a plan for interaction and interact with them. This approach gives us a few advantages: 1. Skyvern can operate on websites it’s never seen before, as it’s able to map visual elements to actions necessary to complete a workflow, without any customized code 2. Skyvern is resistant to website layout changes, as there are no pre-determined XPaths or other selectors our system is looking for while trying to navigate 3. Skyvern leverages LLMs to reason through interactions to ensure we can cover complex situations. Examples include: 1. If you wanted to get an auto insurance quote from Geico, the answer to a common question “Were you eligible to drive at 18?” could be inferred from the driver receiving their license at age 16 2. If you were doing competitor analysis, it’s understanding that an Arnold Palmer 22 oz can at 7/11 is almost definitely the same product as a 23 oz can at Gopuff (even though the sizes are slightly different, which could be a rounding error!) Want to see examples of Skyvern in action? Jump to #real-world-examples-of- skyvern

Kaizen is an open-source project that helps teams ensure quality in their software delivery by providing a suite of tools for code review, test generation, and end-to-end testing. It integrates with your existing code repositories and workflows, allowing you to streamline your software development process. Kaizen generates comprehensive end-to-end tests, provides UI testing and review, and automates code review with insightful feedback. The file structure includes components for API server, logic, actors, generators, LLM integrations, documentation, and sample code. Getting started involves installing the Kaizen package, generating tests for websites, and executing tests. The tool also runs an API server for GitHub App actions. Contributions are welcome under the AGPL License.

**Description:** PromptFuzz is an automated tool that generates high-quality fuzz drivers for libraries via a fuzz loop constructed on mutating LLMs' prompts. The fuzz loop of PromptFuzz aims to guide the mutation of LLMs' prompts to generate programs that cover more reachable code and explore complex API interrelationships, which are effective for fuzzing. **Features:** * **Multiply LLM support** : Supports the general LLMs: Codex, Inocder, ChatGPT, and GPT4 (Currently tested on ChatGPT). * **Context-based Prompt** : Construct LLM prompts with the automatically extracted library context. * **Powerful Sanitization** : The program's syntax, semantics, behavior, and coverage are thoroughly analyzed to sanitize the problematic programs. * **Prioritized Mutation** : Prioritizes mutating the library API combinations within LLM's prompts to explore complex interrelationships, guided by code coverage. * **Fuzz Driver Exploitation** : Infers API constraints using statistics and extends fixed API arguments to receive random bytes from fuzzers. * **Fuzz engine integration** : Integrates with grey-box fuzz engine: LibFuzzer. **Benefits:** * **High branch coverage:** The fuzz drivers generated by PromptFuzz achieved a branch coverage of 40.12% on the tested libraries, which is 1.61x greater than _OSS-Fuzz_ and 1.67x greater than _Hopper_. * **Bug detection:** PromptFuzz detected 33 valid security bugs from 49 unique crashes. * **Wide range of bugs:** The fuzz drivers generated by PromptFuzz can detect a wide range of bugs, most of which are security bugs. * **Unique bugs:** PromptFuzz detects uniquely interesting bugs that other fuzzers may miss. **Usage:** 1. Build the library using the provided build scripts. 2. Export the LLM API KEY if using ChatGPT or GPT4. 3. Generate fuzz drivers using the `fuzzer` command. 4. Run the fuzz drivers using the `harness` command. 5. Deduplicate and analyze the reported crashes. **Future Works:** * **Custom LLMs suport:** Support custom LLMs. * **Close-source libraries:** Apply PromptFuzz to close-source libraries by fine tuning LLMs on private code corpus. * **Performance** : Reduce the huge time cost required in erroneous program elimination.

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