Best Artificial Intelligence Software for JUnit
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Compare the Top Artificial Intelligence Software that integrates with JUnit as of June 2025
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This a list of Artificial Intelligence software that integrates with JUnit. Use the filters on the left to add additional filters for products that have integrations with JUnit. View the products that work with JUnit in the table below.
What is Artificial Intelligence Software for JUnit?
Artificial Intelligence (AI) software is computer technology designed to simulate human intelligence. It can be used to perform tasks that require cognitive abilities, such as problem-solving, data analysis, visual perception and language translation. AI applications range from voice recognition and virtual assistants to autonomous vehicles and medical diagnostics. Compare and read user reviews of the best Artificial Intelligence software for JUnit currently available using the table below. This list is updated regularly.
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1
Parasoft
Parasoft
Parasoft helps organizations continuously deliver high-quality software with its AI-powered software testing platform and automated test solutions. Supporting embedded and enterprise markets, Parasoft’s proven technologies reduce the time, effort, and cost of delivering secure, reliable, and compliant software by integrating everything from deep code analysis and unit testing to UI and API testing, plus service virtualization and complete code coverage, into the delivery pipeline. A powerful unified C and C++ test automation solution for static analysis, unit testing and structural code coverage, Parasoft C/C++test helps satisfy compliance with industry functional safety and security requirements for embedded software systems.Starting Price: $125/user/mo -
2
Harness
Harness
Harness is an AI-native software delivery platform that helps engineering teams achieve excellence by automating and streamlining the entire software delivery lifecycle. It enables continuous integration, continuous delivery, and GitOps for multi-cloud, multi-region deployments with increased speed and reliability. Harness simplifies infrastructure as code, database DevOps, and artifact management to improve collaboration and reduce errors. The platform offers AI-powered testing, incident response, chaos engineering, and feature management to enhance quality and resilience. Harness also provides cloud cost management, security testing orchestration, and developer insights to optimize performance and governance. Trusted by leading enterprises, Harness accelerates innovation while reducing manual effort and risk. -
3
Launchable
Launchable
You can have the best developers in the world, but every test is making them slower. 80% of your software tests are pointless. The problem is you don't know which 80%. We find the right 20% using your data so that you can ship faster. We have shrink-wrapped predictive test selection, a machine learning-based approach being used at companies like Facebook so that it can be used by any company. We support multiple languages, test runners, and CI systems. Just bring Git to the table. Launchable uses machine learning to analyze your test failures and source code. It doesn't rely on code syntax analysis. This means it's trivial for Launchable to add support for almost any file-based programming language. It also means we can scale across teams and projects with different languages and tools. Out of the box, we currently support Python, Ruby, Java, JavaScript, Go, C, and C++, and we regularly add support for new languages. -
4
Roost.ai
Roost.ai
Roost.ai is an AI-powered software testing platform that leverages generative AI and large language models (LLMs) like GPT-4, Gemini, Claude, and Llama3 to automate the generation of unit and API test cases, ensuring 100% test coverage. It integrates seamlessly with existing DevOps tools such as GitHub, GitLab, Bitbucket, Azure DevOps, Terraform, and CloudFormation, enabling automated test updates in response to code changes and pull requests. Roost.ai supports multiple programming languages, including Java, Go, Python, Node.js, and C#, and can generate tests for various frameworks like JUnit, TestNG, pytest, and Go's standard testing package. It also facilitates the creation of ephemeral test environments on demand, streamlining acceptance testing and reducing the time and resources required for quality assurance. By automating repetitive testing tasks and enhancing test coverage, Roost.ai empowers development teams to focus on innovation and accelerate release cycles. -
5
Selenic
Parasoft
Selenium tests are often unstable and difficult to maintain. Parasoft Selenic fixes common Selenium problems within your existing projects with no vendor lock. When your team is using Selenium to develop and test the UI for your software applications, you need confidence that your testing process is identifying real issues, creating meaningful and appropriate tests, and reducing test maintenance. While Selenium offers many benefits, you want to get more out of your UI testing while leveraging your current practice. Find the real UI issues and get quick feedback on test execution so you can deliver better software faster with Parasoft Selenic. Improve your existing library of Selenium web UI tests, or quickly create new ones, with a flexible Selenium companion that integrates seamlessly with your environment. Parasoft Selenic fixes common Selenium problems with AI-powered self-healing to minimize runtime failures, test impact analysis to dramatically reduce test execution time, etc. -
6
Symflower
Symflower
Symflower enhances software development by integrating static, dynamic, and symbolic analyses with Large Language Models (LLMs). This combination leverages the precision of deterministic analyses and the creativity of LLMs, resulting in higher quality and faster software development. Symflower assists in identifying the most suitable LLM for specific projects by evaluating various models against real-world scenarios, ensuring alignment with specific environments, workflows, and requirements. The platform addresses common LLM challenges by implementing automatic pre-and post-processing, which improves code quality and functionality. By providing the appropriate context through Retrieval-Augmented Generation (RAG), Symflower reduces hallucinations and enhances LLM performance. Continuous benchmarking ensures that use cases remain effective and compatible with the latest models. Additionally, Symflower accelerates fine-tuning and training data curation, offering detailed reports.
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