Alternatives to Deequ

NUnit serves as a unit-testing framework compatible with all .Net languages, having originally been adapted from JUnit. The latest production release, version 3, has undergone a complete overhaul, introducing numerous features and accommodating a diverse array of .NET platforms. As a member of the .NET Foundation, the NUnit Project benefits from guidance and support aimed at securing its future. The achievement of NUnit is attributed to the diligent efforts of countless contributors and team members, with the Core Team expressing gratitude for the invaluable assistance and contributions that have propelled NUnit to its current level of success. As of the latest statistics, various NUnit packages have amassed over 126 million downloads on NuGet.org, a milestone made possible by the commitment of numerous volunteers who generously share their expertise and time. Additionally, NUnit is classified as Open Source software, and version 3 is distributed under the MIT license, ensuring its accessibility and collaborative development. Such community involvement underscores the project's importance and fosters continued innovation within the .NET ecosystem.

aqua, with its AI-powered technology, is a cutting-edge Test Management System built to streamline and boost QA processes. Perfect for both large and small businesses, especially in highly regulated sectors like Fintech, MedTech, and GovTech, aqua excels in: - Organizing and managing custom testing workflows - Handling various testing scales and complexities, - Managing comprehensive test data sets - Ensuring detailed insights through advanced reporting - Transitioning from manual to automated testing All of this becomes effortless with Aqua. Additionaly, it stands out with "Capture" - simplified 'single-click' bug tracking and reproducing solution. Seamlessly integrating with popular platforms like JIRA, Selenium, and Jenkins, and supported by REST API, aqua enhances QA efficiency, significantly reducing time spent on routine tasks and accelerating software release cycles by 200%. Take away your pain of testing! Try aqua today!

Catch2 serves primarily as a unit testing framework tailored for C++, yet it also incorporates fundamental micro-benchmarking capabilities and straightforward BDD macros. Its primary strength lies in its user-friendly and intuitive design. Test identifiers do not require adherence to valid naming conventions, assertions resemble standard C++ boolean expressions, and the use of sections allows for a localized approach to managing setup and teardown code within tests. Currently, you are working on the devel branch where version 3 is under development. This upcoming version introduces several major updates, the most notable being that Catch2 transitions from a single-header library to a conventional library structure featuring multiple headers and a separately compiled implementation. Getting started is quick and straightforward; you only need to download two files, integrate them into your project, and you're ready to go, all without any external dependencies. As long as your environment supports C++14 and includes the C++ standard library, you can write test cases as self-registering functions or methods if that suits your style. This flexibility in coding approaches enhances the framework's usability for various programming preferences.

Early is an innovative AI-powered solution that streamlines the creation and upkeep of unit tests, thereby improving code integrity and speeding up development workflows. It seamlessly integrates with Visual Studio Code (VSCode), empowering developers to generate reliable unit tests directly from their existing codebase, addressing a multitude of scenarios, including both standard and edge cases. This methodology not only enhances code coverage but also aids in detecting potential problems early in the software development lifecycle. Supporting languages such as TypeScript, JavaScript, and Python, Early works effectively with popular testing frameworks like Jest and Mocha. The tool provides users with an intuitive experience, enabling them to swiftly access and adjust generated tests to align with their precise needs. By automating the testing process, Early seeks to minimize the consequences of bugs, avert code regressions, and enhance development speed, ultimately resulting in the delivery of superior software products. Furthermore, its ability to quickly adapt to various programming environments ensures that developers can maintain high standards of quality across multiple projects.

Refraction serves as a powerful code-generation tool tailored for developers, employing AI to assist in writing code. This innovative platform enables users to produce unit tests, documentation, refactor existing code, and much more. It supports code generation in 34 programming languages, including Assembly, C#, C++, CoffeeScript, CSS, Dart, Elixir, Erlang, Go, GraphQL, Groovy, Haskell, HTML, Java, JavaScript, Kotlin, LaTeX, Less, Lua, MatLab, Objective-C, OCaml, Perl, PHP, Python, R Lang, Ruby, Rust, Sass/SCSS, Scala, Shell, SQL, Swift, and TypeScript. With Refraction, thousands of developers globally are streamlining their workflows, utilizing AI to automate tasks such as documentation creation, unit testing, and code refactoring. This tool not only enhances efficiency but also allows programmers to concentrate on more critical aspects of software development. By leveraging AI, you can refactor, optimize, fix, and style-check your code effortlessly. Additionally, it facilitates the generation of unit tests compatible with various testing frameworks and helps clarify the intent of your code, making it more accessible for others. Embrace the capabilities of Refraction and transform your coding experience today.

Pytest is an invaluable tool for enhancing your programming skills, as it simplifies the creation of both basic tests and complicated functional tests for various applications and libraries. The framework’s ability to provide detailed assertion introspection means you can rely solely on standard assert statements for all your testing needs. It offers thorough information regarding failed assertions, automatically identifies test modules and functions, and features modular fixtures that help manage both small and parameterized long-lived test resources effectively. Additionally, pytest can seamlessly execute unittest (including trial) and nose test suites, and it is compatible with Python versions 3.6 and above, as well as PyPy 3. Its rich plugin architecture boasts over 315 external plugins and is backed by a vibrant community of users. Furthermore, the maintainers of pytest, along with thousands of other packages, have partnered with Tidelift to provide commercial support and maintenance for the open-source dependencies integral to your projects. By leveraging pytest, you can save valuable time, minimize risks, and enhance the overall health of your codebase, all while ensuring that the developers of the specific dependencies you rely on are compensated for their work. This commitment to community and support truly sets pytest apart as a leader in the testing framework landscape.

Bright Security offers a developer-focused Dynamic Application Security Testing (DAST) solution designed to help organizations rapidly and cost-effectively deliver secure applications and APIs. Its methodology allows for swift and iterative scans to detect critical security vulnerabilities early in the software development lifecycle (SDLC), all while maintaining high quality and rapid delivery. Bright enables Application Security (AppSec) teams to implement governance for the protection of APIs and web applications, empowering developers to take charge of security testing and the necessary remediation processes. In contrast to traditional DAST solutions that are tailored for AppSec specialists and often prove to be cumbersome to implement—resulting in vulnerabilities being discovered late in the development cycle—Bright's DAST solution is crafted to thrive in a DevOps environment. It can be integrated as soon as the Unit Testing phase and can be utilized throughout the SDLC, continually learning and optimizing from each scan. By facilitating the early detection and remediation of vulnerabilities within the SDLC, Bright not only mitigates risk but also does so in a more economical and less labor-intensive manner. This proactive approach ultimately strengthens the overall security posture of organizations while streamlining the development process.

Consistently develop, evaluate, launch, and oversee applications across all platforms. Integrate with repositories such as GitHub, Bitbucket, GitLab, or Azure DevOps, enabling cloud-based builds with every code commit. Seamlessly execute unit tests, distribute to testers and stores, or assess your user interface on actual devices. Utilize a feature branch for building and testing, a beta branch for tester distribution, and the master branch for App Store submission. Tailor the process to suit your team's unique workflow requirements. After implementing changes, are you certain your application still functions correctly? How does it perform on a legacy device like the LG G2? Discover the answers by utilizing a hosted device lab that offers access to thousands of genuine iOS and Android devices for comprehensive testing. You'll obtain detailed test results, high-resolution screenshots from each stage of the process, as well as performance analytics. Furthermore, regardless of whether you choose private distribution, public open betas, or platforms like Microsoft Intune, TestFlight, Google Play, or the App Store, App Center ensures that launching your application is a seamless and enjoyable experience. With its robust features, you can confidently focus on enhancing your app's functionality and user experience.

The toolkit is available as a collection of resources distributed through the Maven Central repository. It necessitates Java version 7 or higher to run tests, which must be executed using either JUnit or TestNG. For guidance on incorporating the library into a Java project, refer to the section on Running tests with JMockit. This tutorial explores the various APIs offered by the library, illustrated through example tests that utilize Java 8. The primary API consists of a singular annotation that facilitates the automatic creation and setup of the objects intended for testing. Additionally, there exists the mocking API, commonly referred to as the "Expectations" API, which is designed for tests that engage with mocked dependencies. Furthermore, a compact faking API, known as the "Mockups" API, is provided for generating and utilizing fake implementations, thereby mitigating the full resource demands of external components. Overall, this toolkit enhances testing efficiency by streamlining the setup process and providing versatile mocking capabilities.

Maintain high-quality code while adhering to agile development cycles. Jtest's extensive Java testing tools will ensure that you code flawlessly at every stage of Java software development. Streamline Compliance with Security Standards. Ensure that your Java code conforms to industry security standards. Automated generation of compliance verification documentation Get Quality Software Out Faster Java testing tools can be integrated to detect defects faster and more efficiently. Reduce time and costs by avoiding costly and complicated problems later. Increase your return on unit testing. Create a set of JUnit test suites that are easy to maintain and optimize for code coverage. Smart test execution allows you to get faster feedback from CI as well as within your IDE. Parasoft Jtest integrates seamlessly into your development ecosystem and CI/CD pipeline for real-time, intelligent feedback about your testing and compliance progress.

QUnit is a robust and user-friendly JavaScript testing framework designed for simplicity. Its setup is hassle-free, requiring no configuration for Node.js projects and only minimal adjustments for those intended for browser use. You have the flexibility to execute your tests in various environments, whether it’s Node.js, a web browser, or even within a web worker. This allows you to validate your code in the context in which it operates. With adaptable APIs available for custom assertions, runners, and reporters, QUnit can be tailored to meet specific requirements. The onboarding process for QUnit in Node.js projects is swift and straightforward; just begin by installing the QUnit package via npm. Once installed, you can initiate the test suite using the QUnit CLI. It’s advisable to execute the QUnit command through an npm script, which will seamlessly locate the QUnit command from your local dependencies. To enhance your understanding of how to organize tests and make assertions, refer to the API documentation. Additionally, QUnit adheres to the Node.js Long-term Support (LTS) schedule, ensuring it is compatible with current, active LTS, and maintenance LTS releases, thus providing long-term reliability for your projects. This commitment to support makes QUnit an ideal choice for developers seeking a dependable testing framework.

TestNG is a robust testing framework that draws inspiration from both JUnit and NUnit while introducing a range of new features that enhance its power and usability; among these are annotations and the ability to execute tests in large thread pools, utilizing various policies such as dedicating a thread to each method or assigning one thread per test class. This framework allows for the validation of multithread safety in code, offers flexible test configurations, and supports data-driven testing through the use of the @DataProvider annotation, along with parameter handling. Its execution model is highly efficient, eliminating the need for traditional TestSuites, and it is compatible with an array of tools and plugins, including Eclipse, IDEA, and Maven, enhancing its integration into existing workflows. Additionally, TestNG incorporates BeanShell for increased flexibility and leverages default JDK functionalities for runtime operations and logging, thus minimizing external dependencies while also supporting dependent methods for application server testing. As a comprehensive solution, TestNG is tailored to accommodate all types of testing scenarios, including unit, functional, end-to-end, and integration tests, making it an essential tool for developers and testers alike.

Diffblue Cover analyzes Java programs and creates unit regression tests that accurately reflect the current behavior. The CLI tool is 100% self-configuring and can be used in your Gradle or Maven environment. The CLI tool automates the test-writing process and provides a speed boost to organizations working towards achieving DevOps goals such as CI/CD. The CLI tool fits in a CI pipeline and protects the entire codebase from regressions by shifting testing to the left. Diffblue Cover's unit tests for regression are fast and verify any code changes immediately. This allows users to detect unwanted changes in code behavior as soon as possible. Teams can save even more time by having tests automatically maintained.

Mockito is a highly regarded mocking framework that is celebrated for its user-friendly nature. It allows developers to create elegant tests through a straightforward and intuitive API. One of the standout features of Mockito is that it helps avoid confusion, as the resulting tests are easy to read and the verification errors it generates are clear and concise. For those interested, further information can be found regarding its features and the reasons behind its popularity. It consistently ranks among the top Java libraries, transcending just the realm of testing tools. A comprehensive analysis conducted in late 2013 examined 30,000 GitHub projects, revealing that while Mockito achieved the 9th spot in the overall rankings, it is important to note that both mockito-core and mockito-all refer to the same tool, effectively placing Mockito at an impressive 4th position—outperforming well-known libraries like Guava and Spring. This analysis serves as a testament to the significant influence Mockito has on Java unit testing practices each day, illustrating its widespread adoption and importance in the development community. The ongoing relevance of Mockito in modern software development cannot be overstated.

Storybook serves as a dedicated environment for creating user interfaces in isolation, allowing developers to address challenging scenarios and edge cases effectively. You can design components and pages without the hassle of managing data, APIs, or business logic complexities. It enables the rendering of components in specific states that are difficult to replicate within a typical application setup. By saving use cases as stories in straightforward JavaScript, you can easily return to them throughout development, testing, and quality assurance phases. Additionally, you can enhance your workflow with addons that facilitate automated testing and integration with preferred tools. These stories act as a practical and reproducible method to document UI edge cases comprehensively. After writing a story, you can leverage it for automated testing, as each one essentially provides a valuable test case. This allows you to swiftly review stories and ensure your UI appears as intended. You can identify pixel-level UI changes by comparing image snapshots of these stories, and you can incorporate them into your unit tests to validate intricate functionalities. Ultimately, stories illustrate the actual operation of UIs rather than just their intended functionality, simplifying the process of collecting feedback and reproducing issues. Furthermore, this approach fosters a collaborative environment where team members can share insights and improvements based on real user interactions.

BaseRock.ai is an innovative platform specializing in AI-enhanced software quality that streamlines both unit and integration testing, allowing developers to create and run tests straight from their favorite IDEs. Utilizing cutting-edge machine learning algorithms, it assesses codebases to produce detailed test cases that guarantee thorough code coverage and enhanced quality. By integrating effortlessly with CI/CD workflows, BaseRock.ai aids in the early identification of bugs, which can lead to a reduction in QA expenditures by as much as 80% while also increasing developer efficiency by 40%. The platform boasts features such as automated test creation, instant feedback, and compatibility with a variety of programming languages, including Java, JavaScript, TypeScript, Kotlin, Python, and Go. Additionally, BaseRock.ai provides a range of pricing options, including a complimentary tier, to suit diverse development requirements. Many top-tier companies rely on BaseRock.ai to improve software quality and speed up the delivery of new features, making it a valuable asset in the tech industry. Its commitment to continuous improvement ensures that it remains at the forefront of software testing solutions.

Spark Streaming extends the capabilities of Apache Spark by integrating its language-based API for stream processing, allowing you to create streaming applications in the same manner as batch applications. This powerful tool is compatible with Java, Scala, and Python. One of its key features is the automatic recovery of lost work and operator state, such as sliding windows, without requiring additional code from the user. By leveraging the Spark framework, Spark Streaming enables the reuse of the same code for batch processes, facilitates the joining of streams with historical data, and supports ad-hoc queries on the stream's state. This makes it possible to develop robust interactive applications rather than merely focusing on analytics. Spark Streaming is an integral component of Apache Spark, benefiting from regular testing and updates with each new release of Spark. Users can deploy Spark Streaming in various environments, including Spark's standalone cluster mode and other compatible cluster resource managers, and it even offers a local mode for development purposes. For production environments, Spark Streaming ensures high availability by utilizing ZooKeeper and HDFS, providing a reliable framework for real-time data processing. This combination of features makes Spark Streaming an essential tool for developers looking to harness the power of real-time analytics efficiently.

GitAuto is an AI-driven coding assistant that seamlessly connects with GitHub (and optionally Jira) to assess backlog tickets or issues, evaluate your repository's structure and code, and autonomously create and review pull requests, usually completing this process in around three minutes per ticket. It is capable of managing bug fixes, implementing feature requests, and enhancing test coverage. You can activate it through specific issue labels or selections on a dashboard, allowing it to write code or unit tests, initiate a pull request, execute GitHub Actions, and continuously rectify any failing tests until they succeed. Supporting ten programming languages, such as Python, Go, Rust, and Java, GitAuto is free for basic use, with paid plans available for those requiring a greater volume of pull requests and additional enterprise functionalities. Adhering to a strict zero data-retention policy, it processes your code through OpenAI without retaining it. Built to speed up delivery by allowing teams to address technical debt and backlogs without the need for extensive engineering resources, GitAuto functions as an AI backend engineer that drafts, tests, and refines code, thereby significantly enhancing development efficiency. This innovative tool not only streamlines workflows but also empowers teams to focus on more strategic tasks.

Jasmine strives to adhere to semantic versioning principles as closely as possible, which entails designating major versions (such as 1.0, 2.0, etc.) for significant changes or breaking alterations. In most cases, Jasmine's updates are categorized as minor releases (like 2.3, 2.4, etc.), with major versions being relatively rare. Generally, Jasmine does not remove support for specific browser or Node versions, except during major releases. However, there are exceptions for Node versions that have reached their end of life, browsers that can no longer be installed or tested in our continuous integration builds, those that no longer receive security updates, and browsers that operate exclusively on outdated operating systems. While we will make reasonable attempts to ensure Jasmine remains functional in these environments, we do not guarantee a major release in the event of any compatibility issues. This approach allows us to balance progress with stability for our users.

The unittest framework for unit testing was influenced by JUnit and shares characteristics with other prominent unit testing frameworks across various programming languages. It offers features like test automation, the ability to share setup and teardown procedures, the grouping of tests into collections, and ensures that tests operate independently from the reporting framework. A test fixture is essential for preparing the environment required for one or more tests, along with any necessary cleanup processes, which might include setting up temporary databases, creating directories, or initiating server processes. A test suite serves as a compilation of test cases and other test suites, designed to group tests that should be run together. Meanwhile, a test runner acts as a mechanism to manage the execution of tests and communicate the results to the user. This runner can function through a graphical interface, a command-line interface, or may return a specific value to reflect the outcomes of the tests executed. Overall, the unittest framework simplifies the testing process while promoting organized and efficient test execution.

JustMock simplifies the process of isolating your testing environment, enabling you to concentrate on the specific logic you wish to assess. It integrates effortlessly with your preferred unit testing framework, streamlining both unit testing and mocking to be quick and straightforward. You can mock a wide array of components, including non-virtual methods, sealed classes, static methods and classes, as well as non-public members and types across the board, including those in MsCorLib. It serves as an ideal solution for unit testing your .NET applications, regardless of whether you are working with intricate legacy systems or code crafted with best practices in mind. The JustMock Debug Window is particularly useful for troubleshooting, as it provides insights into the arguments used when calling mock objects, as well as identifying potential issues like why a mock isn't invoked or is invoked multiple times. Furthermore, JustMock gives you essential feedback regarding the thoroughness and completeness of your unit tests, making it an indispensable tool for organizations aiming to maintain high standards in code quality. By leveraging its capabilities, teams can enhance their testing strategies and ensure more reliable software development outcomes.

dbForge Unit Tests for SQL Server is a graphical user interface (GUI) tool, available as an add-in for SQL Server Management Studio (SSMS). Built on the tSQLt framework, it allows developers to write unit tests in T-SQL within the familiar SSMS interface. Key Features: - Isolated Unit Testing: Avoid affecting the actual database. - Simultaneous Testing: Perform multiple unit tests simultaneously. - DevOps Integration: Integrate unit tests into DevOps processes. - Command Line Execution: Run unit tests via the command line. - T-SQL-based: Write unit tests in T-SQL without additional tools. - tSQLt Framework Management: Easily install and uninstall the tSQLt framework for writing unit tests. - Example Collection: Access a collection of unit test examples for quick standard task performance. - Safe Refactoring: Ensure safe refactoring in large databases. The tool includes a Test List Manager, a tab within the SSMS interface meant for creating, editing, managing, and running unit tests. This integration allows teams to work entirely within SSMS, eliminating the need to learn and switch between different tools, thus fostering a more comfortable environment and efficient performance.

Unit testing made simple: You can write tests without modifying your existing code, including legacy systems. This applies to static methods, private methods, non-virtual methods, out parameters, and even class members and fields. Our professional edition is available at no cost for developers globally, alongside options for paid support packages. By enhancing your code integrity, you can consistently produce high-quality code. You can create entire object models with just a single command, enabling you to mock static methods, private methods, constructors, events, LINQ queries, reference arguments, and more, whether they are live or future elements. The automated test suggestion feature tailors recommendations specifically for your code, while our intelligent test runner efficiently executes only the tests that are impacted, providing you with rapid feedback. Additionally, our coverage tool allows you to visualize your code coverage directly in your editor as you develop, ensuring that you keep track of your testing progress. This comprehensive approach not only saves time but also significantly enhances the reliability of your software.

PHPUnit necessitates the activation of the dom and json extensions, which are typically enabled by default, alongside the pcre, reflection, and spl extensions that are also standard and cannot be disabled without modifying PHP's build system or source code. Additionally, to generate code coverage reports, the Xdebug extension (version 2.7.0 or newer) and the tokenizer extension must be present, while the ability to create XML reports relies on the xmlwriter extension. Writing unit tests is fundamentally a best practice for developers to detect and resolve bugs, refactor code, and provide documentation for a unit of software being tested. Ideally, unit tests should encompass all potential execution paths within a program to maximize effectiveness. Generally, a single unit test is aligned with one specific path in a particular function or method. Nonetheless, it is important to recognize that a test method may not function as a completely isolated or independent unit, as there can often be subtle dependencies between various test methods that stem from the underlying implementation of a test scenario. This interconnectedness can sometimes lead to challenges in maintaining test integrity and reliability.

Appsurify utilizes its patented AI technology to identify the segments of an application that have been altered following each developer commit, enabling it to automatically select and run only the tests pertinent to those modifications within the CI Pipeline. By narrowing down to a targeted set of tests influenced by each developer's changes, Appsurify enhances the optimization of CI Pipelines, eliminating the delays caused by automated testing and allowing Builds to operate more swiftly and effectively. The traditional approach to Automation Testing and CI Pipelines often hampers productivity due to prolonged completion times, which results in delayed feedback for bug detection and pushes release schedules further down the line. With Appsurify, the collaboration between QA and DevOps is made more efficient, as it facilitates focused test execution in critical areas, ensuring that bugs are identified early and that CI/CD pipelines maintain a smooth and efficient flow. This innovation leads to a more agile development process, ultimately contributing to a faster and more reliable software delivery cycle.

Components within a software system rarely function independently; instead, they interact with one another to fulfill their tasks effectively. During unit testing, it is often unnecessary to utilize the actual implementations of these collaborating components, as we typically have confidence in their reliability. Instead, mock objects serve as stand-ins for the collaborators associated with the unit being tested. To effectively evaluate a unit in isolation or to create an adequate testing environment, it is essential to replicate the behavior of these collaborators within the test framework. A Mock Object acts as a test-focused substitute for a collaborator, designed to replicate the functionalities of the original object in an uncomplicated manner. Unlike a stub, which merely provides preset responses, a Mock Object additionally checks if it is utilized correctly during the test process. EasyMock was the pioneer in offering dynamic Mock Object generation, sparing developers from the tedious task of manually creating Mock Objects or writing code for their generation. By employing Java's proxy mechanism, EasyMock facilitates the on-the-fly creation of Mock Objects, streamlining the testing process and enhancing efficiency. This innovation not only simplifies the testing workflow but also ensures a greater degree of control and accuracy during unit tests.

This allows for parallel collaborative development to reduce rework, visualize, reduce conflict, and lower team risk. It also improves team velocity and throughput. It preserves the integrity of baseline configurations, simplifies rollback, recovery, streamlines approval, and keeps integrity when automating builds or deployments. It is much easier to identify issues early in the lifecycle. CM allows issues can be identified as soon a code commit is made via Pulse. All actions drive shift left: CI build, unit testing and code review. Integration with developer friendly repositories, IDEs (such git, IntelliJ Eclipse, Microsoft.net), detailed auditing, logging, immutable versioning, and history Reduces preparation time and effort for regulatory compliance and audits with a comprehensive audit trail and tamperproof history.

HUnit serves as a unit testing framework tailored for Haskell, drawing inspiration from the widely used JUnit framework within the Java ecosystem. Users who are already acquainted with Haskell will find HUnit straightforward to adopt, even if they lack prior experience with JUnit. A development approach that prioritizes testing proves to be most efficient when the process of creating, modifying, and running tests is seamless. JUnit was instrumental in introducing test-first development practices in Java, and HUnit functions as its counterpart for Haskell, a language known for its purely functional paradigm. Like JUnit, HUnit allows developers to effortlessly craft tests, assign names, organize them into suites, and run them while the framework automatically validates the outcomes. The test specification in HUnit boasts greater conciseness and flexibility compared to JUnit, which is a direct benefit of Haskell's design. Although HUnit currently supports a text-based test controller, it is structured to facilitate straightforward extensions in the future. To maximize efficiency, it is recommended to run the tests collectively as a suite.

All members of the team can perform robust automated testing on desktop, mobile, and web applications. This is regardless of whether they have any experience with functional test automation tools. Ranorex Studio is an all in one solution that provides codeless automation tools and a complete IDE. Ranorex Studio's industry-leading object recognition system and shareable object repository make it possible to automate GUI testing, regardless of whether you are using legacy applications or the latest mobile and web technologies. Ranorex Studio supports cross browser testing with integrated Selenium WebDriver integration. Easy data-driven testing can be done using CSV files, Excel spreadsheets, or SQL database files. Ranorex Studio supports keyword-driven testing. Our tools for collaboration enable test automation engineers to create reusable code modules, and share them with their team. Get a 30-day free trial to get started with automation testing.

AppHarbor is a comprehensive Platform as a Service (PaaS) designed specifically for .NET applications. With AppHarbor, developers can effortlessly deploy and scale a wide array of standard .NET applications in the cloud. This platform is popular among thousands of businesses and developers, catering to everything from simple personal blogs to large-scale, high-traffic web applications. It allows for instant deployment and scaling of .NET applications while integrating seamlessly with preferred version control tools. Adding additional features through add-ons is equally straightforward. Developers can push their .NET and Windows code to AppHarbor using various version control systems such as Git, Mercurial, Subversion, or Team Foundation Server, supported by a complimentary Git service and integrations with services like Bitbucket, CodePlex, and GitHub. Upon receiving the code, AppHarbor utilizes a build server to compile it, and if the compilation is successful, all unit tests within the compiled assemblies are executed. Users can track the progress and results of the build and unit tests via the application dashboard. Additionally, AppHarbor can trigger service hooks that you configure to keep you updated on the build outcomes. This robust functionality makes AppHarbor a valuable tool for developers seeking efficiency and reliability in their deployment processes.

Our platform possesses the capability to comprehend your code on a sophisticated level, effortlessly managing edge cases within your coding setup to guarantee the timely production of flawless software. With UnitWise at your command, enjoy the swift delivery of exceptional software solutions. No longer will you have to endure the monotonous task of manually crafting test cases or depend on prompts for test code generation. As you immerse yourself in coding, UnitWise serves as your reliable testing partner, offering immediate and relevant test codes that enhance your development process. Simply choose the class you wish to test and generate test cases in your IDE with a single click. You can also observe how failed test cases behave and make necessary adjustments to eliminate errors. Furthermore, you can pinpoint bugs in your code, thereby increasing software reliability while effortlessly correcting any identified issues. We place a strong emphasis on data security, steadfastly maintaining the integrity and confidentiality of your code throughout the testing process. In this way, UnitWise not only facilitates coding but also enriches your overall software development experience.

Confident AI has developed an open-source tool named DeepEval, designed to help engineers assess or "unit test" the outputs of their LLM applications. Additionally, Confident AI's commercial service facilitates the logging and sharing of evaluation results within organizations, consolidates datasets utilized for assessments, assists in troubleshooting unsatisfactory evaluation findings, and supports the execution of evaluations in a production environment throughout the lifespan of LLM applications. Moreover, we provide over ten predefined metrics for engineers to easily implement and utilize. This comprehensive approach ensures that organizations can maintain high standards in the performance of their LLM applications.

The AgitarOne product suite empowers you to enhance safety, efficiency, and intelligence in the development and upkeep of your Java applications. The AgitarOne JUnit Generator produces comprehensive JUnit tests for your code, which aids in identifying regressions and streamlines the process of improving your code while minimizing maintenance costs. Additionally, AgitarOne Agitator assists developers in grasping their code's behavior during the writing phase, effectively helping to avoid bugs and reduce code complexity that could lead to future maintenance challenges. The AgitarOne family stands out as the premier solution for creating, utilizing, and managing the unit tests essential for achieving true agility in development. With its automated JUnit generation feature, you can establish a protective "safety net" before you begin modifying existing code, ensuring greater reliability and stability in your projects. This proactive approach not only saves time but also fosters a more confident coding environment.

Harness the power of your data and create innovative artificial intelligence (AI) solutions using Azure Databricks, where you can establish your Apache Spark™ environment in just minutes, enable autoscaling, and engage in collaborative projects within a dynamic workspace. This platform accommodates multiple programming languages such as Python, Scala, R, Java, and SQL, along with popular data science frameworks and libraries like TensorFlow, PyTorch, and scikit-learn. With Azure Databricks, you can access the most current versions of Apache Spark and effortlessly connect with various open-source libraries. You can quickly launch clusters and develop applications in a fully managed Apache Spark setting, benefiting from Azure's expansive scale and availability. The clusters are automatically established, optimized, and adjusted to guarantee reliability and performance, eliminating the need for constant oversight. Additionally, leveraging autoscaling and auto-termination features can significantly enhance your total cost of ownership (TCO), making it an efficient choice for data analysis and AI development. This powerful combination of tools and resources empowers teams to innovate and accelerate their projects like never before.

WireMock is a tool designed to simulate HTTP-based APIs, which some may refer to as a mock server or a service virtualization solution. It proves invaluable for maintaining productivity when a necessary API is either unavailable or incomplete. The tool also facilitates the testing of edge cases and failure scenarios that a live API might not consistently reproduce. Its speed can significantly decrease build times, transforming hours of work into mere minutes. MockLab builds on WireMock by providing a hosted API simulator that features an easy-to-use web interface, allows for team collaboration, and requires no installation. The API is fully compatible and can replace the WireMock server effortlessly with just a single line of code. You can operate WireMock from a variety of environments, including Java applications, JUnit tests, Servlet containers, or as an independent process. It offers the ability to match request URLs, HTTP methods, headers, cookies, and bodies through numerous strategies. Additionally, it provides robust support for both JSON and XML formats, making it simple to get started by capturing traffic from an existing API. Overall, WireMock serves as a crucial resource for developers seeking to streamline their API testing processes.

Scapy is an advanced, interactive tool designed for packet manipulation. It excels in crafting and interpreting packets across a multitude of protocols, facilitating their transmission, capturing data, and correlating requests with responses, among various other functionalities. This versatile program can efficiently perform traditional tasks such as scanning, tracerouting, probing, unit testing, executing attacks, and discovering networks, effectively replacing tools like hping, a significant portion of nmap, arpspoof, arp-sk, arping, tcpdump, tshark, and p0f. Furthermore, Scapy stands out in executing specialized tasks that other utilities struggle with, including the transmission of invalid frames and the injection of custom 802.11 frames, as well as employing techniques like VLAN hopping combined with ARP cache poisoning or VOIP decoding over WEP-encrypted channels. It is compatible with various operating systems, including Linux, Windows, OSX, and most Unix variants that utilize libpcap. Notably, the same codebase supports both Python 2 and Python 3, showcasing its versatility. The development of Scapy is managed through the Git version control system, with its primary repository available on GitHub, allowing for collaborative improvements and updates. As a result, users benefit from continuous enhancements that keep pace with the evolving landscape of network security and analysis.

Testing and managing test data for IBM i, IBM iSeries, and AS/400 systems requires thorough validation of complex applications, extending down to the underlying data. TestBench for IBM i offers a robust and reliable solution for test data management, verification, and unit testing, seamlessly integrating with other tools to ensure overall application quality. Instead of duplicating the entire live database, you can focus on the specific data that is essential for your testing needs. By selecting or sampling data while maintaining complete referential integrity, you can streamline the testing process. You can easily identify which fields require protection and employ various obfuscation techniques to safeguard your data effectively. Additionally, you can monitor every insert, update, and delete action, including the intermediate states of the data. Setting up automatic alerts for data failures through customizable rules can significantly reduce manual oversight. This approach eliminates the tedious save and restore processes and helps clarify any inconsistencies in test results that stem from inadequate initial data. While comparing outputs is a reliable way to validate test results, it often involves considerable effort and is susceptible to mistakes; however, this innovative solution can significantly reduce the time spent on testing, making the entire process more efficient. With TestBench, you can enhance your testing accuracy and save valuable resources.

Nova AI streamlines various testing activities that often hinder developers throughout the implementation phase. Our solutions operate seamlessly in the background, handling these tasks without requiring developers to navigate multiple interfaces or tools. You can effortlessly create and run unit, integration, and end-to-end tests all from one cohesive platform. Not only are existing tests executed, but newly created ones are also processed, providing valuable results and insights. We ensure complete isolation of your data, with a strict policy against sharing it. Additionally, we have implemented SSL encryption for data in transit and use industry-standard 256-bit AES encryption for data at rest, while also working towards achieving SOC 2 Type 2 compliance. Your security and data integrity are our top priorities, allowing you to focus on development without concerns about privacy.

The LDRA tool suite stands as the premier platform offered by LDRA, providing a versatile and adaptable framework for integrating quality into software development from the initial requirements phase all the way through to deployment. This suite encompasses a broad range of functionalities, which include requirements traceability, management of tests, adherence to coding standards, evaluation of code quality, analysis of code coverage, and both data-flow and control-flow assessments, along with unit, integration, and target testing, as well as support for certification and regulatory compliance. The primary components of this suite are offered in multiple configurations to meet various software development demands. Additionally, a wide array of supplementary features is available to customize the solution for any specific project. At the core of the suite, LDRA Testbed paired with TBvision offers a robust combination of static and dynamic analysis capabilities, along with a visualization tool that simplifies the process of understanding and navigating the intricacies of standards compliance, quality metrics, and analyses of code coverage. This comprehensive toolset not only enhances software quality but also streamlines the development process for teams aiming for excellence in their projects.

Creating unit tests can be a challenging endeavor, and at times, it may require compromising good design principles solely to enhance testability. While good design often aligns with improved testability, this correlation does not hold true in every situation. For instance, the use of final classes and methods can become problematic, with private methods occasionally needing to be altered to protected or shifted to a collaborator unnecessarily. Additionally, static methods are best avoided altogether due to the constraints imposed by various frameworks. PowerMock is a robust framework that supplements other mocking libraries, such as EasyMock, offering enhanced capabilities. Through the utilization of a custom classloader and bytecode manipulation, PowerMock allows for the mocking of static methods, constructors, final classes, private methods, and the removal of static initializers, among other features. Importantly, because it employs a custom classloader, users can integrate it without needing to modify their IDEs or continuous integration systems, thus streamlining the process of implementation. Ultimately, the ability to mock such diverse elements can significantly improve the flexibility and effectiveness of unit testing efforts.

MLlib, the machine learning library of Apache Spark, is designed to be highly scalable and integrates effortlessly with Spark's various APIs, accommodating programming languages such as Java, Scala, Python, and R. It provides an extensive range of algorithms and utilities, which encompass classification, regression, clustering, collaborative filtering, and the capabilities to build machine learning pipelines. By harnessing Spark's iterative computation features, MLlib achieves performance improvements that can be as much as 100 times faster than conventional MapReduce methods. Furthermore, it is built to function in a variety of environments, whether on Hadoop, Apache Mesos, Kubernetes, standalone clusters, or within cloud infrastructures, while also being able to access multiple data sources, including HDFS, HBase, and local files. This versatility not only enhances its usability but also establishes MLlib as a powerful tool for executing scalable and efficient machine learning operations in the Apache Spark framework. The combination of speed, flexibility, and a rich set of features renders MLlib an essential resource for data scientists and engineers alike.

Streamlining the delivery process of DataStage minimizes time, expenses, risks, uncertainties, and stress. Make sure every job adheres to your coding standards, or tap into MettleCI's comprehensive library of best practices. You can manage your code effortlessly through Git right from the DataStage Designer interface, eliminating the need for any command line complications. Transition your code seamlessly from Development to Production with a fully automated delivery pipeline. Ditch the sequences and let MettleCI automatically create and execute your job schedules. Trust MettleCI to manage the rigorous testing process, ensuring your builds are deployable without issues. Enjoy a hassle-free experience with no command lines, complicated configurations, or hacking involved. Simplify the testing, building, and deployment processes with an intuitive point-and-click interface that enhances productivity. In this way, you can focus on innovation rather than getting bogged down by technicalities.

You can perform almost any task that you would typically handle manually in a web browser using Puppeteer! The lightweight version, known as Puppeteer-core, is designed for those who want to launch an existing browser installation or connect to a remote browser. It's crucial to ensure that the version of puppeteer-core you choose aligns with the browser version you plan to use. For individuals already acquainted with other browser testing frameworks, Puppeteer will seem quite familiar. You start by creating a Browser instance, opening web pages, and then using Puppeteer's API to manipulate those pages. By default, Puppeteer downloads a specific Chromium version, ensuring that its API functions correctly right from the start. To utilize Puppeteer with an alternative version of Chrome or Chromium, you simply need to specify the path to the executable when you create a Browser instance. This flexibility allows for a tailored testing experience that can adapt to different project requirements.

Apache Mahout is an advanced and adaptable machine learning library that excels in processing distributed datasets efficiently. It encompasses a wide array of algorithms suitable for tasks such as classification, clustering, recommendation, and pattern mining. By integrating seamlessly with the Apache Hadoop ecosystem, Mahout utilizes MapReduce and Spark to facilitate the handling of extensive datasets. This library functions as a distributed linear algebra framework, along with a mathematically expressive Scala domain-specific language, which empowers mathematicians, statisticians, and data scientists to swiftly develop their own algorithms. While Apache Spark is the preferred built-in distributed backend, Mahout also allows for integration with other distributed systems. Matrix computations play a crucial role across numerous scientific and engineering disciplines, especially in machine learning, computer vision, and data analysis. Thus, Apache Mahout is specifically engineered to support large-scale data processing by harnessing the capabilities of both Hadoop and Spark, making it an essential tool for modern data-driven applications.

xUnit.net is a community-driven, open-source unit testing framework designed for the .NET Framework, available at no cost. Developed by the creator of NUnit v2, it represents the cutting-edge approach to unit testing for languages such as C#, F#, and VB.NET, among others. Additionally, xUnit.net integrates seamlessly with tools like ReSharper, CodeRush, TestDriven.NET, and Xamarin. As a member of the .NET Foundation, it adheres to their established code of conduct, ensuring a collaborative and respectful environment for developers. This makes xUnit.net an excellent choice for modern software development practices in the .NET ecosystem.