DeepUnit

Freemium

Twitter

Facebook

Copy Link

DeepUnitAI streamlines the process of creating unit tests by automating the generation of Jest unit tests, which can be run through GitHub Actions, a VS Code plugin, or an npm package. It simplifies developers' workflows by installing onto a GitHub repository and automatically creating pull requests containing the generated unit tests, ensuring that the process integrates seamlessly with the developers' current workflows.

How DeepUnitAI can help you:

Automates the writing of Jest unit tests, saving time and effort.
Integrates with VS Code and GitHub, with future support for Jasmine and Gitlab.
Simplifies the workflow by generating pull requests with the needed unit tests.

Why choose DeepUnitAI: Key Features:

AI-driven unit test generation enhances accuracy and efficiency.
Supports any TypeScript project using Jest.
Provides different methods to run tests, including GitHub Action, VS Code plugin, or an npm package.
Streamlined installation and operation with minimal developer intervention required.

Who should choose DeepUnitAI:

Developers who utilize TypeScript and Jest for their unit testing.
Teams looking for an AI-assisted approach to automate test creation.
Users of VS Code and GitHub who would benefit from seamless integration into their development environment.

About DeepUnit

Website

https://www.deepunit.ai

Release Date

February 2024

Pricing

Freemium

Related fields

Related News

Understanding Amazon Q Developer

Join the DZone community and get the full member experience. Join For Free If you are like me, a developer who often has to balance productivity and curiosity in the workplace, then you are always on the lookout for tools that actually make life easier. So, when Amazon came up with Amazon Q Developer, a new AI-powered coding companion that can be embedded in popular IDEs like Visual Studio Code, I was more than just interested. Having used it for a week now, I can safely say it's like having a personal coding buddy who can give you some pretty helpful tips when you need them. But what is Amazon Q Developer, and why should anyone care? Stay tuned and find out. What Exactly Is Amazon Q Developer? Amazon Q Developer is not just a fancy autocompleting tool; it is a smart pair programmer who sits inside your IDE and suggests context-aware code recommendations, snippets, unit test suggestions, and even some troubleshooting tips. With the help of the advanced generative AI, Amazon has developed a simple-to-use coding assistant that is able to comprehend the context of your project. What I liked most about Amazon Q Developer was the speed of the service and the level of understanding. It felt like the AI knew what I was planning to do before I actually did. Features That Matter Amazon Q Developer comes with a simple and effective design of the workplace. Here's a list of some of the great features it has: Intelligent Code Generation From a prompt or even partial comments, Amazon Q writes valid code that is relevant to your project. You will not have to look for solutions to your coding problems on Stack Overflow while in the middle of a coding spree. Integrated Testing and Debugging Amazon Q Developer also suggests test cases and debugging tips within your workflow. The AI comes with suggestions, great unit tests for your Python and Java functions. gives very specific debugging tips that are relevant to your code problem (something that I clearly need). Seamless Documentation Amazon Q Developer knows the value of documentation. The command creates well-described and organized comments within your codebase, keeping your project clean and understandable so that future you (or your teammates) will know what's going on and won't get lost. Effective Code Review and Refactoring There are times when you would want to detect and correct code problems before you actually commit them. You can simply use to detect and suggest a solution to the problems, or to properly rearrange your Java project from one version to the other. Handy Cheat Sheet: Commands at Your Fingertips Here's a quick, practical cheat sheet of commands you'll love: Additionally, Amazon Q Developer integrates advanced features that are easily accessible within your IDE's context menu just by clicking on the right button. Some of the options include: How Do You Set It Up? It's easy -- here's an easy-to-follow guide on how to set up Amazon Q Developer in Visual Studio Code: Install the Amazon Q Developer Extension To do this, in VS Code, go to Extensions (Ctrl + Shift + X), then search for "Amazon Q Developer". Install the extension, and restart/reload VSCode if required. Authenticate With AWS After the installation, use the Amazon Q sidebar icon to authenticate yourself using your AWS credentials through a simple OAuth flow. This includes two options: 1. Builder ID: Individual Customers With Free Tier Set up the Flow for the Amazon Q Developer with AWS Builder ID 2. AWS IAM Identity Center: Professional Customers With Pro Tier Set up Flow for Amazon Q Developer Authentication with IAM Identity Center Prerequisites * AWS Account: You must have an AWS Account with IAM Identity Center activated. * If IAM Identity Center is not activated: 1. Set Up IAM Identity Center and Developer Pro Subscription These can be found in the AWS Console under IAM Identity Center > Settings > Identity Source tab (listed as the AWS access portal URL). 2: Authenticate in Visual Studio Code 3: Browser Authentication 4: Return to Visual Studio Code * Switch back to your Visual Studio Code IDE. * You are now successfully logged into Amazon Q with a Pro license. * A Q Chat window should automatically open. Configure Your Workspace The AI of Amazon Q Developer learns your workspace structure, so all you have to do is open your project, and the AI will take it from there. For example: * Open your source code or a GitHub repository. * Select code, right click, send to Amazon Q to explain the code, or open the file and ask Q to explain the code. Begin Using Commands The cheat sheet commands are /dev, /test, /debug, /doc, and /transform, and you can use them in your comments or in the Amazon Q Developer chat panel. You can also use contextual AI tools by right-clicking within your code. Conclusion In my opinion, Amazon Q Developer is a genuine tool that actually brings value and does not act as a blocking tool for your development. It simplifies the tasks that would otherwise distract you and hinder your ability to concentrate on the actual problem-solving. Of course, no AI tool is perfect, but the context-aware intelligence, convenience, and real productivity benefits that Amazon Q Developer brings to the table make it a tool worth integrating into your daily development workflow. Try it out -- your future self (and your codebase) will appreciate it.

DZone

Thu, 31 Jul, 6:53 PM UTC

AI powered QA for agile software testing with Functionize

https://news-cdn.moonbeam.co/AI-Agents-to-Modernize-Quality-Assurance-with-Functionize-App-Developer-Magazine_o9315rd1.jpg Artificial Intelligence AI powered QA for agile software testing with Functionize Friday, September 5, 2025 Trey Abbe Functionize is helping enterprises, including Fortune 500 and Global 2000 organizations, reduce testing complexity, accelerate software delivery, and improve software quality through AI powered QA. Functionize is an AI-powered platform designed for the intelligent creation of software test cases. It empowers enterprises, including Fortune 500 and Global 2000 organizations, to accelerate software delivery while maintaining quality by reducing testing maintenance, increasing automation, and allowing both technical and non-technical users to contribute to the quality assurance process. Unlike traditional script-based automation tools, Functionize uses advanced machine learning, natural language processing, and agentic AI to support complex test flows and integrate with system integrators. The company recently announced the close of a $41 million Series B funding round, bringing total funding to more than $67 million. The round was led by Mumford Investments and LHH Investments, with additional participation from existing investors Canvas Ventures and Wipro Ventures. The funding will support continued development and adoption of AI-driven enterprise test automation. Addressing QA challenges with autonomous testing Organizations face increasing pressure to deliver software quickly and reliably. Accelerated release cycles, driven by agile development and continuous integration/continuous delivery (CI/CD) practices, place significant demands on quality assurance teams. Traditional test automation methods often require ongoing script maintenance and technical expertise, which can limit overall efficiency. Functionize addresses these challenges by providing a platform that enables autonomous test generation, execution, maintenance, and reporting. The platform allows organizations to expand automation coverage, reduce the manual effort required to maintain tests, and accelerate release cycles while integrating into enterprise workflows. Impact on cusotmers: efficiency, coverage, and collaboration Functionize is designed to help organizations reduce the complexity and cost of software testing while maintaining reliability. The platform's AI agents are intended to learn from application behavior, which may help improve testing efficiency and reduce manual effort. Organizations using Functionize have reported potential benefits such as: Reduced time required for test creation and maintenance. Expanded coverage of automated testing across multiple applications. Increased participation in testing processes by both technical and non-technical team members. These reported benefits may support enterprise efforts to maintain software quality while accelerating software delivery. Agentic AI in Action Functionize's agentic AI platform enables autonomous test generation, execution, and self-healing with minimal human intervention. Unlike traditional tools that rely on static scripts, the platform adapts to changes in applications and workflows, providing immediate value. The platform has been used to execute large volumes of AI-driven actions, reducing manual testing effort while improving accuracy and consistency. Its scalability is particularly relevant for large enterprises managing extensive digital ecosystems. AI trends that are shaping enterprise testing AI-driven automation is increasingly being applied in enterprise software testing. Unlike traditional automation, which relies on predefined scripts, AI-powered approaches can adapt to changes in applications and workflows. Industry research shows that organizations are exploring AI-powered testing solutions to improve efficiency and maintain quality in complex digital environments. Some platforms, including Functionize, offer solutions designed to integrate into enterprise workflows. An insight from Tamas Cser: scaling AI for enterprise QA "We focus on providing scalable, AI-driven testing solutions that integrate into enterprise development processes," said Tamas Cser, CEO of Functionize. "Our platform supports automated test generation, execution, and maintenance in a way that complements existing workflows, allowing organizations to improve efficiency and consistency in quality assurance processes." Expanding AI beyond functional testing In the broader software testing industry, AI is increasingly being applied beyond functional testing, including areas such as performance testing, security testing, and compliance validation. These areas typically require substantial resources, and intelligent automation may help improve efficiency and consistency across multiple testing domains. Looking ahead, AI-driven platforms may also be integrated more closely into development and DevOps workflows, potentially supporting early identification of issues and alignment with continuous delivery pipelines. Partnerships with enterprise software providers and cloud platforms could enhance accessibility and integration of AI-driven automation tools. $41 million Series B funding expands capabilities for AI powered QA for agile software testing with Functionize The Series B funding will be applied toward several initiatives: Advance the development of Functionize's agentic automation platform to generate, execute, and maintain tests with minimal manual intervention. Scale the company's cloud infrastructure to meet growing enterprise demand. Scale go-to-market teams and build additional data science and engineering teams. Support expansion into new markets and industry verticals. Enhance integrations with leading applications and platform providers. An investor perspective on AI-powered test automation "We invest in teams developing innovative enterprise solutions," said JD Mumford, Partner at Mumford Investments. "Functionize applies AI to enterprise test automation at scale, addressing operational challenges that many organizations face with traditional approaches. The platform provides autonomous test creation, execution, and maintenance, helping enterprises manage complex application environments." In conclusion, Functionize provides an AI-driven platform designed to support enterprises in modernizing software quality assurance. By automating test generation, execution, and maintenance, the platform is intended to help improve delivery schedules, increase efficiency, and expand coverage of automated testing. Functionize Platform Overview Become a subscriber of App Developer Magazine for just $5.99 a month and take advantage of all these perks. MEMBERS GET ACCESS TO- Exclusive content from leaders in the industry - Q&A articles from industry leaders - Tips and tricks from the most successful developers weekly - Monthly issues, including all 90+ back-issues since 2012 - Event discounts and early-bird signups - Gain insight from top achievers in the app store - Learn what tools to use, what SDK's to use, and more Subscribe here AI powered QA for agile software testing with Functionize, Artificial Intelligence, Approach, Software development, Quality assurance, Code Quality Improvement, Generative AI, Enterprise AI Solutions, AI Solutions, Machine Learning Share

App Developer Magazine

Fri, 5 Sept, 9:24 PM UTC

LambdaTest Introduces KaneAI: Revolutionizing Software Testing with AI

LambdaTest, an enterprise cloud platform, has launched KaneAI, an AI-powered test agent designed to streamline and enhance the software testing process. This innovative tool aims to improve efficiency and reduce manual effort in debugging and test creation.

2 Sources

Thu, 22 Aug, 12:02 AM UTC

Test-Driven Generation: Adopting TDD With GenAI - DZone

You know, over the past few years, software development has gone through some pretty exciting changes. New tools and methodologies have popped up, all aiming to make our lives easier, streamline processes, and boost code quality. One of the big players in this space has been Test-Driven Development (TDD). If you're not familiar, TDD is where developers write test cases for functionality before they actually write the code. It's a neat idea, but let's be real -- TDD can be tough to get the hang of! From my own experiences, I gotta admit: TDD is hard! It demands time, practice, and a whole lot of discipline to master. But here's the thing -- the rise of Generative AI is opening up some awesome new possibilities to make this technique even better. By combining 1) TDD, 2) Pair Programming, and 3) Generative AI, I want to introduce you to a new approach: Test-Driven Generation (TDG). Great question! TDG is an alternative development practice that brings generative AI right into the heart of the development life cycle. Instead of us, the human developers, writing both the tests and the code, we step into the role of specifiers. We outline the requirements and specifications, and then the AI takes over to generate the corresponding tests and code. TDG is still based on the familiar principles of TDD, but with a twist: the developer pairs up with a generative AI model. This AI buddy takes on the heavy lifting of generating test cases and the code itself, leaving us to focus on the higher-level specs and logic. As you can see, the loop isn't all that different from how we're currently using AI to generate code. The key difference is that this process still requires a bit of discipline from TDD -- specifically, generating the test first, then the code. By combining the strengths of generative AI with traditional development practices, TDG offers a ton of advantages over conventional methodologies like TDD and pair programming. Many of these benefits are inherited from the use of TDD but with some exciting new twists. TDG speeds up the development cycle by offloading the tasks of writing code and tests to the AI. This allows us to focus on refining specifications and overseeing the development process rather than getting bogged down in the nitty-gritty coding details. Since the AI generates test cases right from the start, the code is inherently designed to meet those tests. This reduces the risk of missing edge cases or producing buggy code. It's like having a safety net that catches issues before they become problems. TDG provides a constant feedback loop where we see test results immediately after the AI generates code. This allows for quick course corrections, ensuring that the code aligns with the evolving specifications. It also helps tackle the non-deterministic nature of AI code generation, making the whole process more predictable. Using Generative AI is known to help reduce cognitive load, and TDG is no exception. We can focus on high-level design, architecture, and strategic problem-solving while letting the AI handle routine tasks like writing test cases and boilerplate code. This reduces fatigue and allows us to be more productive in the long run. I've known many teams that struggled to adopt TDD because of its inherent challenges. But with TDG, we can address several of these key drawbacks by leveraging Generative AI to automate many parts of the process. Here's how TDG can mitigate the major cons of TDD: Writing tests before writing the code adds overhead in the early stages of development, slowing down overall progress. With Generative AI, the AI automates test creation based on the high-level specs we provide. This eliminates much of the manual work involved in writing tests upfront, significantly reducing the time needed to kick off development. Instead of writing each test case manually, we focus on refining specs while the AI generates relevant tests. As project requirements evolve, tests need to be updated frequently. Maintaining a large test suite can become a burden, especially if tests aren't written or maintained properly. The AI in TDG can automatically adjust test cases when specifications change, reducing the need for us to manually rewrite or adjust tests. This makes test maintenance more manageable by having the AI adapt to changes quickly, ensuring that tests stay aligned with evolving functionality. TDD requires developers to have experience in writing effective tests, which can be difficult and time-consuming to learn, especially for newcomers. TDG reduces the learning curve by delegating the test-writing task to the AI. We don't need to master the art of test case writing to the same extent. We can focus on high-level design and logic, letting the AI handle the complexities of generating comprehensive test cases. Successful TDD requires a high level of discipline and consistent practice from the entire team. If even a few team members don't follow the methodology rigorously, the benefits of TDD diminish. TDG automates a significant portion of the process, reducing the need for strict discipline. The AI can generate tests consistently, ensuring that no steps are skipped. This allows the team to focus on refining code and design while the AI maintains a consistent testing framework. TDG reduces the dependency on human discipline by making certain tasks automatic. You might be wondering, "Why should we bother with TDG when Generative AI can already pump out code?" That's a fair question, and here's why TDG stands out: While Generative AI can quickly produce code, it doesn't inherently guarantee that the code is correct, reliable, or meets all specified requirements. TDG incorporates the creation of test cases before code generation, ensuring that the AI-generated code is validated against these tests. This approach catches bugs and edge cases early, leading to more robust and reliable software. TDG provides a structured workflow that integrates the principles of Test-Driven Development with Generative AI. This process ensures that development follows a clear specification-to-test-to-code sequence, maintaining the discipline of TDD but now powered by AI. By generating tests first, TDG helps identify issues at the earliest stage of development. This proactive approach reduces the time we spend on debugging and fixing errors after the fact. Simply generating code without accompanying tests can lead to more time spent diagnosing problems later on. In summary, Test-Driven Generation (TDG) blends the best of Test-Driven Development (TDD), Pair Programming, and Generative AI to improve the development process. Instead of writing both the tests and code ourselves, we give the AI the specs, and it generates the tests and the code that passes them, one-by-one. This means we get clean, well-tested code faster while keeping the benefits of TDD to cut down on time spent debugging and fixing confusing errors. So, what do you think? Would you like to give this TDG technique a try? Let me know in the comments below! I'm excited to hear your thoughts and experiences.

DZone

Fri, 11 Oct, 4:16 PM UTC

Gentrace Raises $8M to Revolutionize AI App Testing and Collaboration

Gentrace, a developer platform for testing and monitoring AI applications, has secured $8 million in Series A funding to expand its LLM testing capabilities and make AI development more accessible to non-technical teams.

2 Sources

Wed, 11 Dec, 12:06 AM UTC

Similar products

Octomind

An AI-powered tool designed to automate Playwright end-to-end testing, improving reliability and efficiency for web applications.

Contact for Pricing

Welltested

Welltested is an innovative AI-powered testing tool designed specifically for Flutter developers, offering autonomous generation of unit, widget, and integration tests to enhance code quality and robustness.

Contact for Pricing

ZeroStep

ZeroStep enhances Playwright tests with AI, enabling script creation via plain-text instructions for more resilient E2E testing.

Contact for Pricing

PerfAI

Automate API testing across governance, contracts, releases, testing, performance, etc.

Contact for Pricing

UserTesting AI

UserTesting AI leverages artificial intelligence to enhance UX research, providing deep insights by analyzing video, audio, text, and behavioral data.

Contact for Pricing

Your Daily Dose of Curated AI News

Don’t drown in AI news. We cut through the noise - filtering, ranking and summarizing the most important AI news, breakthroughs and research daily. Spend less time searching for the latest in AI and get straight to action.

The Outpost

News

About