Best AI tools for< Build Tests >

testRigor is an AI-based test automation tool that allows users to create and execute test cases using plain English instructions. It leverages generative AI in software testing to automate test creation and maintenance, offering features such as no code/codeless testing, web, mobile, and desktop testing, Salesforce automation, and accessibility testing. With testRigor, users can achieve test coverage faster and with minimal maintenance, enabling organizations to reallocate QA engineers to build API tests and increase test coverage significantly. The tool is designed to simplify test automation, reduce QA headaches, and improve productivity by streamlining the testing process.

Assessment Systems is an online testing platform that provides cost-effective, AI-driven solutions to develop, deliver, and analyze high-stakes exams. With Assessment Systems, you can build and deliver smarter exams faster, thanks to modern psychometrics and AI like computerized adaptive testing, multistage testing, or automated item generation. You can also deliver exams flexibly: paper, online testing unproctored, online proctored, and test centers (yours or ours). Assessment Systems also offers item banking software to build better tests in less time, with collaborative item development brought to life with versioning, user roles, metadata, workflow management, multimedia, automated item generation, and much more.

Celp is a contextually aware AI-driven unit test generation tool designed for Typescript Node.js projects. It intelligently parses and deeply understands your code, saving you time and ensuring code stability. It uses an agentic design pattern to build context through parsing with Abstract Syntax Trees and intermediary AI prompting. Celp focuses on essential context, formulates detailed plans, and automatically runs and resolves tests. It generates unit tests from selection, reuses existing code, and learns as you use it.

Diffblue Cover is an autonomous AI-powered unit test writing tool for Java development teams. It uses next-generation autonomous AI to automate unit testing, freeing up developers to focus on more creative work. Diffblue Cover can write a complete and correct Java unit test every 2 seconds, and it is directly integrated into CI pipelines, unlike AI-powered code suggestions that require developers to check the code for bugs. Diffblue Cover is trusted by the world's leading organizations, including Goldman Sachs, and has been proven to improve quality, lower developer effort, help with code understanding, reduce risk, and increase deployment frequency.

Datumbox is a machine learning platform that offers a powerful open-source Machine Learning Framework written in Java. It provides a large collection of algorithms, models, statistical tests, and tools to power up intelligent applications. The platform enables developers to build smart software and services quickly using its REST Machine Learning API. Datumbox API offers off-the-shelf Classifiers and Natural Language Processing services for applications like Sentiment Analysis, Topic Classification, Language Detection, and more. It simplifies the process of designing and training Machine Learning models, making it easy for developers to create innovative applications.

TestArmy is an AI-driven software testing platform that offers an army of testing agents to help users achieve software quality by balancing cost, speed, and quality. The platform leverages AI agents to generate Gherkin tests based on user specifications, automate test execution, and provide detailed logs and suggestions for test maintenance. TestArmy is designed for rapid scaling and adaptability to changes in the codebase, making it a valuable tool for both technical and non-technical users.

bottest.ai is an AI-powered chatbot testing tool that focuses on ensuring quality, reliability, and safety in AI-based chatbots. The tool offers automated testing capabilities without the need for coding, making it easy for users to test their chatbots efficiently. With features like regression testing, performance testing, multi-language testing, and AI-powered coverage, bottest.ai provides a comprehensive solution for testing chatbots. Users can record tests, evaluate responses, and improve their chatbots based on analytics provided by the tool. The tool also supports enterprise readiness by allowing scalability, permissions management, and integration with existing workflows.

Vapi is a Voice AI tool designed specifically for developers. It enables developers to interact with their code using voice commands, making the coding process more efficient and hands-free. With Vapi, developers can perform various tasks such as writing code, debugging, and running tests simply by speaking. The tool is equipped with advanced natural language processing capabilities to accurately interpret and execute voice commands. Vapi aims to revolutionize the way developers work by providing a seamless and intuitive coding experience.

Hubble is an all-in-one user research software that provides tools for continuous discovery. It offers a wide range of features such as in-product research, contextual surveys, user targeting, prototype tests, usability tests, and more. Hubble empowers product teams to collect valuable insights from users to build better products. The platform also includes resources like guides, templates, demo videos, and customer stories to help users maximize the benefits of user research.

MyHeritage is an online family history website that allows users to build family trees, search historical records, and take DNA tests. The website has a large database of historical records, including birth, marriage, and death certificates, as well as census records and immigration records. MyHeritage also offers a variety of tools to help users build their family trees, including a search engine, a record matching tool, and a collaboration tool. The website also offers a variety of DNA tests, including a basic ancestry test, a health and ancestry test, and a mitochondrial DNA test. MyHeritage is a valuable resource for anyone who is interested in learning more about their family history.

SnapXam is an AI-powered math tutor that helps students learn math and physics step-by-step. It offers a variety of features, including a math solver, step-by-step solutions, and video explanations. SnapXam is available on iOS and Android devices.

Langtail is a platform that helps developers build, test, and deploy AI-powered applications. It provides a suite of tools to help developers debug prompts, run tests, and monitor the performance of their AI models. Langtail also offers a community forum where developers can share tips and tricks, and get help from other users.

VERA is an AI-powered career coach and educational companion that guides users through their career journey, from identifying the ideal career path to securing the dream job. It offers features such as resume building, interview preparation, career discovery tests, and personalized recommendations based on individual strengths. VERA caters to students, professionals, educational institutions, and enterprises, providing tailored guidance and support for career development and growth.

Vanta is a trust management platform that helps businesses automate compliance, streamline security reviews, and build trust with customers. It offers a range of features to help businesses manage risk and prove security in real time, including: * **Compliance automation:** Vanta automates up to 90% of the work for security and privacy frameworks, making it easy for businesses to achieve and maintain compliance. * **Real-time monitoring:** Vanta provides real-time visibility into the state of a business's security posture, with hourly tests and alerts for any issues. * **Holistic risk visibility:** Vanta offers a single view across key risk surfaces in a business, including employees, assets, and vendors, to help businesses identify and mitigate risks. * **Efficient audits:** Vanta streamlines the audit process, making it easier for businesses to prepare for and complete audits. * **Integrations:** Vanta integrates with a range of tools and platforms to help businesses automate security and compliance tasks.

Ottic is an AI tool designed to empower both technical and non-technical teams to test Language Model (LLM) applications efficiently and accelerate the development cycle. It offers features such as a 360º view of the QA process, end-to-end test management, comprehensive LLM evaluation, and real-time monitoring of user behavior. Ottic aims to bridge the gap between technical and non-technical team members, ensuring seamless collaboration and reliable product delivery.

BenchLLM is an AI tool designed for AI engineers to evaluate LLM-powered apps by running and evaluating models with a powerful CLI. It allows users to build test suites, choose evaluation strategies, and generate quality reports. The tool supports OpenAI, Langchain, and other APIs out of the box, offering automation, visualization of reports, and monitoring of model performance.

Famewall is a testimonial collection tool that helps businesses gather and showcase customer testimonials on their websites. With Famewall, users can easily collect testimonials in minutes, import reviews from various platforms, collect video testimonials, and display social proof using customizable widgets. The tool aims to build trust with website visitors and convert them into customers by providing a user-friendly platform to manage and share testimonials.

Plumb is a no-code, node-based builder that empowers product, design, and engineering teams to create AI features together. It enables users to build, test, and deploy AI features with confidence, fostering collaboration across different disciplines. With Plumb, teams can ship prototypes directly to production, ensuring that the best prompts from the playground are the exact versions that go to production. It goes beyond automation, allowing users to build complex multi-tenant pipelines, transform data, and leverage validated JSON schema to create reliable, high-quality AI features that deliver real value to users. Plumb also makes it easy to compare prompt and model performance, enabling users to spot degradations, debug them, and ship fixes quickly. It is designed for SaaS teams, helping ambitious product teams collaborate to deliver state-of-the-art AI-powered experiences to their users at scale.

Contentable.ai is a platform for comparing multiple AI models, rapidly moving from prototyping to production, and management of your custom AI solutions across multiple vendors. It allows users to test multiple AI models in seconds, compare models side-by-side across top AI providers, collaborate on AI models with their team seamlessly, design complex AI workflows without coding, and pay as they go.

VideoAsk by Typeform is an interactive video platform that helps streamline conversations and build business relationships at scale. It offers features such as asynchronous interviews, easy scheduling, tagging, gathering contact info, capturing leads, research and feedback, training, customer support, and more. Users can create interactive video forms, conduct async interviews, and engage with their audience through AI-powered video chatbots. The platform is user-friendly, code-free, and integrates with over 1,500 applications through Zapier.

This repository contains an early-phase IREE compiler and runtime plugin for interfacing the AMD AIE accelerator to IREE. It provides architectural overview, developer setup instructions, building guidelines, and runtime driver setup details. The repository focuses on enabling the integration of the AMD AIE accelerator with IREE, offering developers the tools and resources needed to build and run applications leveraging this technology.

Recommenders is a project under the Linux Foundation of AI and Data that assists researchers, developers, and enthusiasts in prototyping, experimenting with, and bringing to production a range of classic and state-of-the-art recommendation systems. The repository contains examples and best practices for building recommendation systems, provided as Jupyter notebooks. It covers tasks such as preparing data, building models using various recommendation algorithms, evaluating algorithms, tuning hyperparameters, and operationalizing models in a production environment on Azure. The project provides utilities to support common tasks like loading datasets, evaluating model outputs, and splitting training/test data. It includes implementations of state-of-the-art algorithms for self-study and customization in applications.

This repository contains an assistant proposal for the Semantic Kernel, allowing the usage of assistants without relying on OpenAI Assistant APIs. It runs locally planners and plugins for the assistants, providing scenarios like Assistant with Semantic Kernel plugins, Multi-Assistant conversation, and AutoGen conversation. The Semantic Kernel is a lightweight SDK enabling integration of AI Large Language Models with conventional programming languages, offering functions like semantic functions, native functions, and embeddings-based memory. Users can bring their own model for the assistants and host them locally. The repository includes installation instructions, usage examples, and information on creating new conversation threads with the assistant.

Stellar Chat is a multi-modal chat application that enables users to create custom agents and integrate with local language models and OpenAI models. It provides capabilities for generating images, visual recognition, text-to-speech, and speech-to-text functionalities. Users can engage in multimodal conversations, create custom agents, search messages and conversations, and integrate with various applications for enhanced productivity. The project is part of the '100 Commits' competition, challenging participants to make meaningful commits daily for 100 consecutive days.

Clapper is an open-source AI story visualization tool that can interpret screenplays and render them into storyboards, videos, voice, sound, and music. It is currently in early development stages and not recommended for general use due to some non-functional features and lack of tutorials. A public alpha version is available on Hugging Face's platform. Users can sponsor specific features through bounties and developers can contribute to the project under the GPL v3 license. The tool lacks automated tests and code conventions like Prettier or a Linter.

fish-ai is a tool that adds AI functionality to Fish shell. It can be integrated with various AI providers like OpenAI, Azure OpenAI, Google, Hugging Face, Mistral, or a self-hosted LLM. Users can transform comments into commands, autocomplete commands, and suggest fixes. The tool allows customization through configuration files and supports switching between contexts. Data privacy is maintained by redacting sensitive information before submission to the AI models. Development features include debug logging, testing, and creating releases.

IBM Generative AI Python SDK is a tool designed for the Tech Preview program for IBM Foundation Models Studio. It brings IBM Generative AI (GenAI) into Python programs, offering various operations and types. Users can start a trial version or request a demo via the provided link. The SDK was recently rewritten and released under V2 in 2024, with a migration guide available. Contributors are welcome to participate in the open-source project by contributing documentation, tests, bug fixes, and new functionality.

The Spring AI project provides a Spring-friendly API and abstractions for developing AI applications. It offers a portable client API for interacting with generative AI models, enabling developers to easily swap out implementations and access various models like OpenAI, Azure OpenAI, and HuggingFace. Spring AI also supports prompt engineering, providing classes and interfaces for creating and parsing prompts, as well as incorporating proprietary data into generative AI without retraining the model. This is achieved through Retrieval Augmented Generation (RAG), which involves extracting, transforming, and loading data into a vector database for use by AI models. Spring AI's VectorStore abstraction allows for seamless transitions between different vector database implementations.

Züs is a high-performance cloud on a fast blockchain offering privacy and configurable uptime. It uses erasure code to distribute data between data and parity servers, allowing flexibility for IT managers to design for security and uptime. Users can easily share encrypted data with business partners through a proxy key sharing protocol. The ecosystem includes apps like Blimp for cloud migration, Vult for personal cloud storage, and Chalk for NFT artists. Other apps include Bolt for secure wallet and staking, Atlus for blockchain explorer, and Chimney for network participation. The QoS protocol challenges providers based on response time, while the privacy protocol enables secure data sharing. Züs supports hybrid and multi-cloud architectures, allowing users to improve regulatory compliance and security requirements.

Apache Airavata is a software framework for executing and managing computational jobs on distributed computing resources. It supports local clusters, supercomputers, national grids, academic and commercial clouds. Airavata utilizes service-oriented computing, distributed messaging, and workflow composition. It includes a server package with an API, client SDKs, and a general-purpose UI implementation called Apache Airavata Django Portal.

TigerGraph CoPilot is an AI assistant that combines graph databases and generative AI to enhance productivity across various business functions. It includes three core component services: InquiryAI for natural language assistance, SupportAI for knowledge Q&A, and QueryAI for GSQL code generation. Users can interact with CoPilot through a chat interface on TigerGraph Cloud and APIs. CoPilot requires LLM services for beta but will support TigerGraph's LLM in future releases. It aims to improve contextual relevance and accuracy of answers to natural-language questions by building knowledge graphs and using RAG. CoPilot is extensible and can be configured with different LLM providers, graph schemas, and LangChain tools.

horde-engine is a wrapper around ComfyUI designed to run inference pipelines visually designed in the ComfyUI GUI. It enables users to design inference pipelines in ComfyUI and then call them programmatically, maintaining compatibility with the existing horde implementation. The library provides features for processing Horde payloads, initializing the library, downloading and validating models, and generating images based on input data. It also includes custom nodes for preprocessing and tasks such as face restoration and QR code generation. The project depends on various open source projects and bundles some dependencies within the library itself. Users can design ComfyUI pipelines, convert them to the backend format, and run them using the run_image_pipeline() method in hordelib.comfy.Comfy(). The project is actively developed and tested using git, tox, and a specific model directory structure.

uTensor is an extremely light-weight machine learning inference framework built on Tensorflow and optimized for Arm targets. It consists of a runtime library and an offline tool that handles most of the model translation work. The core runtime is only ~2KB. The workflow involves constructing and training a model in Tensorflow, then using uTensor to produce C++ code for inferencing. The runtime ensures system safety, guarantees RAM usage, and focuses on clear, concise, and debuggable code. The high-level API simplifies tensor handling and operator execution for embedded systems.

DotRecast is a C# port of Recast & Detour, a navigation library used in many AAA and indie games and engines. It provides automatic navmesh generation, fast turnaround times, detailed customization options, and is dependency-free. Recast Navigation is divided into multiple modules, each contained in its own folder: - DotRecast.Core: Core utils - DotRecast.Recast: Navmesh generation - DotRecast.Detour: Runtime loading of navmesh data, pathfinding, navmesh queries - DotRecast.Detour.TileCache: Navmesh streaming. Useful for large levels and open-world games - DotRecast.Detour.Crowd: Agent movement, collision avoidance, and crowd simulation - DotRecast.Detour.Dynamic: Robust support for dynamic nav meshes combining pre-built voxels with dynamic objects which can be freely added and removed - DotRecast.Detour.Extras: Simple tool to import navmeshes created with A* Pathfinding Project - DotRecast.Recast.Toolset: All modules - DotRecast.Recast.Demo: Standalone, comprehensive demo app showcasing all aspects of Recast & Detour's functionality - Tests: Unit tests Recast constructs a navmesh through a multi-step mesh rasterization process: 1. First Recast rasterizes the input triangle meshes into voxels. 2. Voxels in areas where agents would not be able to move are filtered and removed. 3. The walkable areas described by the voxel grid are then divided into sets of polygonal regions. 4. The navigation polygons are generated by re-triangulating the generated polygonal regions into a navmesh. You can use Recast to build a single navmesh, or a tiled navmesh. Single meshes are suitable for many simple, static cases and are easy to work with. Tiled navmeshes are more complex to work with but better support larger, more dynamic environments. Tiled meshes enable advanced Detour features like re-baking, hierarchical path-planning, and navmesh data-streaming.

This simple package can be used to fix an invalid json string. To know all cases in which this package will work, check out the unit test. Inspired by https://github.com/josdejong/jsonrepair Motivation Some LLMs are a bit iffy when it comes to returning well formed JSON data, sometimes they skip a parentheses and sometimes they add some words in it, because that's what an LLM does. Luckily, the mistakes LLMs make are simple enough to be fixed without destroying the content. I searched for a lightweight python package that was able to reliably fix this problem but couldn't find any. So I wrote one How to use from json_repair import repair_json good_json_string = repair_json(bad_json_string) # If the string was super broken this will return an empty string You can use this library to completely replace `json.loads()`: import json_repair decoded_object = json_repair.loads(json_string) or just import json_repair decoded_object = json_repair.repair_json(json_string, return_objects=True) Read json from a file or file descriptor JSON repair provides also a drop-in replacement for `json.load()`: import json_repair try: file_descriptor = open(fname, 'rb') except OSError: ... with file_descriptor: decoded_object = json_repair.load(file_descriptor) and another method to read from a file: import json_repair try: decoded_object = json_repair.from_file(json_file) except OSError: ... except IOError: ... Keep in mind that the library will not catch any IO-related exception and those will need to be managed by you Performance considerations If you find this library too slow because is using `json.loads()` you can skip that by passing `skip_json_loads=True` to `repair_json`. Like: from json_repair import repair_json good_json_string = repair_json(bad_json_string, skip_json_loads=True) I made a choice of not using any fast json library to avoid having any external dependency, so that anybody can use it regardless of their stack. Some rules of thumb to use: - Setting `return_objects=True` will always be faster because the parser returns an object already and it doesn't have serialize that object to JSON - `skip_json_loads` is faster only if you 100% know that the string is not a valid JSON - If you are having issues with escaping pass the string as **raw** string like: `r"string with escaping\"" Adding to requirements Please pin this library only on the major version! We use TDD and strict semantic versioning, there will be frequent updates and no breaking changes in minor and patch versions. To ensure that you only pin the major version of this library in your `requirements.txt`, specify the package name followed by the major version and a wildcard for minor and patch versions. For example: json_repair==0.* In this example, any version that starts with `0.` will be acceptable, allowing for updates on minor and patch versions. How it works This module will parse the JSON file following the BNF definition: ::= | ::= | | ; Where: ; is a valid real number expressed in one of a number of given formats ; is a string of valid characters enclosed in quotes ; is one of the literal strings 'true', 'false', or 'null' (unquoted) ::=