Best Unit Testing Software - Page 3

Deequ is a library built on top of Apache Spark for defining "unit tests for data", which measure data quality in large datasets. We are happy to receive feedback and contributions. Deequ depends on Java 8. Deequ version 2.x only runs with Spark 3.1, and vice versa. If you rely on a previous Spark version, please use a Deequ 1.x version (legacy version is maintained in legacy-spark-3.0 branch). We provide legacy releases compatible with Apache Spark versions 2.2.x to 3.0.x. The Spark 2.2.x and 2.3.x releases depend on Scala 2.11 and the Spark 2.4.x, 3.0.x, and 3.1.x releases depend on Scala 2.12. Deequ's purpose is to "unit-test" data to find errors early, before the data gets fed to consuming systems or machine learning algorithms. In the following, we will walk you through a toy example to showcase the most basic usage of our library.

Reducing the time, cost, risk, uncertainty, and stress of DataStage delivery. Ensure all jobs comply with your coding standards, or use MettleCI's extensive library of best practices. Manage code in Git, directly from the DataStage Designer client, without any command line headaches. Promote your code from Development to Production using a fully-automated delivery pipeline. Give sequences the boot and allow MettleCI to build and run your job schedule automatically. Let MettleCI handle the heavy lifting of testing to ensure your builds are deployable without problems. No command lines, hacking, or complex config files. Test, build, and deploy using point-and-click.

Nova AI automates many of the non-productive testing tasks that developers face during implementation. Our solutions work behind the scenes and complete these tasks without your developers having to use different interfaces or tools. Automatically generate and execute unit, integration, and end-to-end tests from a single platform. Both current and newly generated tests are executed, with results and insights surfaced. All your data is completely isolated and we never share it. We've enabled SSL-encrypted transit and industry-standard 256-bit AES encrypted at rest, and SOC 2 Type 2 is in progress.

TESSY automates the whole unit test cycle including regression testing for your embedded software in C/C++ on different target systems. As an easy-to-install and easy-to-operate testing tool TESSY guides you through the unit test workflow from the project setup through the test design and execution to the result analysis and reporting. TESSY takes additional care of the complete test organization as well as the test management, including requirements, coverage measurement, and traceability. The TESSY test project management feature provides the possibility to define an individual test project structure based on collections and folders, to define environment variables, and to configure your desired compiler/debugger with the help of the integrated Test Environment Editor. The coverage results are displayed directly within the project structure and the test results are stored within test runs which allows for keeping former (successful) results for reporting.

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.

Gru.ai is an innovative AI-driven platform designed to enhance software development workflows by automating tasks like unit testing, bug fixing, and algorithm development. With tools like Test Gru, Bug Fix Gru, and Assistant Gru, Gru.ai helps developers streamline their processes and improve efficiency. Test Gru automates unit test generation, ensuring superior test coverage while reducing manual effort. Bug Fix Gru automatically identifies and resolves issues directly within your GitHub repositories. Assistant Gru is an AI developer that assists with technical challenges like debugging and coding, delivering reliable and high-quality solutions. Gru.ai is tailored for developers looking to optimize their coding processes and reduce repetitive tasks through the power of AI.

Meet Agile development cycles while maintaining high-quality code. Use Jtest’s comprehensive set of Java testing tools to ensure defect-free coding through every stage of software development in the Java environment. Streamline Compliance With Security Standards. Ensure your Java code complies with industry security standards. Have compliance verification documentation automatically generated. Release Quality Software, Faster. Integrate Java testing tools to find defects faster and earlier. Save time and money by mitigating complicated and expensive problems down the line. Increase Your Return From Unit Testing. Achieve code coverage targets by creating a maintainable and optimized suite of JUnit tests. Get faster feedback from CI and within your IDE using smart test execution. Parasoft Jtest integrates tightly into your development ecosystem and CI/CD pipeline for real-time, intelligent feedback on your testing and compliance progress.

CodeBeaver writes and updates your unit tests. Not only that! It highlights bugs in your Pull Requests by running tests and checking out your code. It works natively with GitHub, GitLab and Bitbucket. The onboarding takes 2 clicks! We currently help 30k GitHub stars and counting.