Search Results for "model based testing tool"
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Showing 1099 open source projects for "model based testing tool"
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Model Explorer
A modern model graph visualizer and debugger
Model Explorer is a visual tool for exploring, debugging, and optimizing ML models deployed on edge devices. Developed by Google AI Edge, it offers a browser-based interface to inspect layer-wise performance, memory usage, and inference timing of TensorFlow Lite and other supported models. It’s a powerful utility for developers optimizing models for constrained environments. -
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Fastbot-Android Open Source Handbook
Testing tool for modeling GUI transitions
Fastbot_Android (Fastbot 2.0) is a model-based automated testing tool by ByteDance designed to discover stability or usability issues in Android apps by modeling GUI transitions rather than relying purely on random interactions. It blends machine learning and reinforcement-learning approaches to build a transition graph of UI states and use that model to intelligently explore possible user interactions — aiming to replicate more human-like usage patterns and uncover hidden bugs, crashes, or edge cases. ... -
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FuzzyAI Fuzzer
A powerful tool for automated LLM fuzzing
FuzzyAI is an open-source fuzzing framework designed to test the security and reliability of large language model applications. The tool automates the process of generating adversarial prompts and input variations to identify vulnerabilities such as jailbreaks, prompt injections, or unsafe model responses. It allows developers and security researchers to systematically evaluate the robustness of LLM-based systems by simulating a wide range of malicious or unexpected inputs. ... -
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Cactus Needle
26m function call model that runs on incredibly small devices
Needle is an experimental 26-million-parameter function-calling model designed to run on extremely small devices such as phones, watches, glasses, and low-power personal AI hardware. It is based on a Simple Attention Network architecture and was distilled from a much larger model to focus on fast, compact tool-use behavior. The project provides open weights, training details, dataset generation resources, and a playground for testing the model with custom tools. ... -
MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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NeuroSploit
NeuroSploit is an advanced, AI-powered penetration testing framework
...Candidate findings are checked through cross-model validation and supporting tool evidence before being reported. It supports black-box, white-box, gray-box, infrastructure, and AI application security testing modes. NeuroSploit also provides a terminal interface, project memory, configurable model providers, structured reports, and a large library of specialized security agents. -
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iperf3
iperf3: A TCP, UDP, and SCTP network bandwidth measurement tool
iperf3 is the official iperf3 repository, a network performance measurement tool for testing maximum achievable bandwidth on IP networks. It supports TCP, UDP, and SCTP testing, making it useful for a wide range of network troubleshooting and benchmarking scenarios. The tool reports throughput, bitrate, packet loss, and other measurement details for each test. iperf3 was redesigned from the original iperf with a smaller codebase, a cleaner architecture, and library functionality that can be... -
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JUnit 5
Programmer-friendly testing framework for Java and the JVM
The 5th major version of the programmer-friendly testing framework for Java and the JVM. JUnit 5 is the next generation of JUnit. The goal is to create an up-to-date foundation for developer-side testing on the JVM. This includes focusing on Java 8 and above, as well as enabling many different styles of testing. Official CI build server for JUnit 5. Used to perform quick checks on submitted pull requests and for build matrices including the latest released OpenJDK and early access builds of... -
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Easy DataSet
A powerful tool for creating datasets for LLM fine-tuning
...The system includes automated question-generation capabilities, hierarchical label trees, and answer generation pipelines that use LLM APIs to produce coherent paired data with customizable templates. Beyond dataset creation, Easy-dataset also provides a built-in evaluation system with model testing and blind-test features, helping teams validate model performance using curated test sets. -
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Stowaway
Multi-hop Proxy Tool for pentesters
Stowaway is a Go-based multi-hop proxy tool created for authorized penetration testing, red-team labs, and security training environments. It uses an admin-and-agent model that allows operators to build a chain of connected nodes across segmented networks. The tool supports active and passive connection modes, making it adaptable to different network layouts. -
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Aimmy
Universal Second Eye for Gamers with Impairments
Aimmy is a source-available AI-based aim alignment tool created for gamers who have difficulty aiming because of physical, visual, coordination, or accessibility-related limitations. It uses computer vision to analyze screen captures and identify target-like objects without directly modifying game files. The project is built in C# with .NET, WPF, DirectML, ONNX, and YOLOv8. -
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PentestGPT
Automated Penetration Testing Agentic Framework Powered by LLMs
PentestGPT is an AI-powered autonomous penetration testing agent designed to perform intelligent, end-to-end security assessments using large language models. Published at USENIX Security 2024, it combines advanced reasoning with an agentic workflow to automate tasks traditionally handled by human pentesters. The platform supports multiple penetration testing categories, including web security, cryptography, reversing, forensics, privilege escalation, and binary exploitation. PentestGPT runs... -
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Synthetic Data Generator
SDG is a specialized framework
...The platform enables developers and data scientists to create artificial datasets that preserve important relationships between variables without containing sensitive personal information. This makes the generated data suitable for tasks such as machine learning model training, testing software systems, sharing datasets across organizations, and conducting research without violating privacy regulations. The system supports multiple generation methods including statistical models, generative adversarial networks, and large language model–based synthesis. It also includes a data processing module capable of handling different data types, preprocessing columns, managing missing values, and converting formats automatically before model training. -
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Locust
Scalable open source load testing tool
Locust is an open source user load testing tool written in Python. The idea behind Locust is to swarm your web site or other systems with attacks from simulated users during a test, with each user behavior defined by you using Python code. This swarming process is then monitored from a web UI in real-time, and will help identify any bottlenecks in your code before real users can come in. -
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CyberStrikeAI
CyberStrikeAI is an AI-native security testing platform built in Go
...It supports role-based testing, letting teams define security roles with tailored tool access and prompts, and includes a skills system that encapsulates specialized testing strategies that the AI can incorporate into its planning. Through comprehensive lifecycle management, results are tracked, aggregated, and visualized, with support for versioned persistence, search, and risk severity scoring. -
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AimAhead
The fastest AI powered Aimbot
AimAhead is an AI-powered aim assist tool designed for high-speed target acquisition. It captures the screen, processes the image through a selected AI model to detect enemies, and then aims towards them. Optimized for NVIDIA graphics cards, AimAhead converts ONNX models to TensorRT engine files for enhanced performance, achieving between 100 to 200 cycles per second depending on the model used. -
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lynis
Security auditing tool for Linux, macOS, and UNIX-based system
Lynis is a battle-tested security tool for systems running Linux, macOS, or Unix-based operating system. It performs an extensive health scan of your systems to support system hardening and compliance testing. The project is open source software with the GPL license and available since 2007. Since Lynis is flexible, it is used for several different purposes. Typical use cases for Lynis include security auditing, compliance testing (e.g. -
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Deepchecks
Test Suites for validating ML models & data
Deepchecks is the leading tool for testing and for validating your machine learning models and data, and it enables doing so with minimal effort. Deepchecks accompany you through various validation and testing needs such as verifying your data’s integrity, inspecting its distributions, validating data splits, evaluating your model and comparing between different models. -
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MCP Chat
Open Source Generic MCP Client for testing & evaluating mcp servers
mcp-chat is an open-source, generic command-line interface (CLI) client designed for testing and evaluating Model Context Protocol (MCP) servers and agents. It allows users to interact with various MCP servers, facilitating seamless communication with AI models. The tool supports both interactive and direct prompt modes, enhancing flexibility in user interactions. -
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MCP Server and GW
An MCP stdio to HTTP SSE transport gateway with example server
mcp-server-and-gw is a Model Context Protocol (MCP) gateway that bridges standard input/output to HTTP Server-Sent Events (SSE) transport. It includes an example MCP server and client, facilitating the development and testing of MCP implementations. This tool is particularly useful for integrating MCP servers with applications like Claude Desktop that may not natively support remote server connections. -
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Giskard
Collaborative & Open-Source Quality Assurance for all AI models
The testing framework dedicated to ML models, from tabular to LLMs. Giskard is an open-source testing framework dedicated to ML models, from tabular models to LLMs. Testing Machine Learning applications can be tedious. Since ML models depend on data, testing scenarios depend on the domain specificities and are often infinite. At Giskard, we believe that Machine Learning needs its own testing framework. -
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Dinky
Dinky makes files smaller
...It supports flexible configuration, allowing users to connect to different model providers and adjust parameters as needed. Dinky also emphasizes speed, enabling rapid iteration cycles that are essential for prompt engineering and prototyping. Its design avoids unnecessary complexity while still providing enough functionality for practical use. Overall, Dinky serves as a compact and efficient tool for exploring and testing AI-driven interactions. -
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Apache Maven
Apache Maven core
Maven is a project development management and comprehension tool. Based on the concept of a project object model: builds, dependency management, documentation creation, site publication, and distribution publication are all controlled from the pom.xml declarative file. Maven can be extended by plugins to utilize a number of other development tools for reporting or the build process. -
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AWS SAM CLI
CLI tool to build, test, debug, and deploy Serverless applications
The AWS Serverless Application Model (SAM) CLI is an open-source CLI tool that helps you develop serverless applications containing Lambda functions, Step Functions, API Gateway, EventBridge, SQS, SNS and more. The AWS Serverless Application Model (SAM) is an open-source framework for building serverless applications. It provides shorthand syntax to express functions, APIs, databases, and event source mappings. -
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BruteForceAI
Advanced LLM-powered brute-force tool combining AI intelligence
BruteForceAI is an open-source security testing tool that applies large language models to the analysis of login forms and authentication flows in web applications. At a high level, the project uses AI to inspect HTML content, identify the relevant form elements, and automate selector discovery so that a tester does not need to hand-map every field before evaluation. It combines that analysis layer with automated credential testing workflows, framing itself as a more adaptive alternative to... -
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kMCP
Kubernetes Controller for building, testing and deploying MCP servers
KMCP is a companion toolchain for building, testing, and deploying MCP servers with a workflow that spans local development through Kubernetes production deployments. It includes a CLI for day-to-day development tasks like scaffolding new MCP projects, managing tools, building container images, and running an MCP server locally for validation. For cluster operations, it includes a Kubernetes controller that manages MCP server lifecycles using a dedicated Custom Resource Definition (CRD),...