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Unit testing enables developers to verify individual component functionality, ensuring each unit behaves as expected. This meticulous assessment detects defects early, enhancing software reliability and stability during development.
Automated unit testing processes make it easier for developers and testers to ensure code reliability, efficiency, and effectiveness. By streamlining testing procedures, developers can swiftly verify functionality, enhancing overall software quality.
Employ advanced automation to generate comprehensive unit test suites quickly, ensuring thorough, robust test coverage and fostering high-quality software.
Seamlessly integrate code coverage tracking with unit testing to rigorously examine every aspect of the codebase and satisfy coverage requirements.
Integrate unit testing with automated regression testing in CI/CD pipelines to quickly identify and resolve issues and accelerate delivery cycles.
Pinpoint essential tests affected by code modifications to expedite testing cycles and improve software reliability with test impact analysis.
Accelerate unit testing with AI-driven capabilities that let developers build test suites effortlessly, adapt to code changes, and automate testing processes.
Leverage GoogleTest, the C++ testing framework your developers prefer, together with Parasoft to meet your full range of testing and verification needs.
Parasoft offers two complementary solutions tailored to different C and C++ workflows: Parasoft C/C++test and Parasoft C/C++test CT. Both enable seamless unit test creation, execution, and regression with powerful stubbing and mocking, IDE integration, and AI-driven automation. Whether teams prefer a proprietary framework or an open-source ecosystem, Parasoft accelerates high-quality software delivery.
Parasoft C/C++test (Proprietary Framework)
C/C++test streamlines unit testing through automated test generation within its robust proprietary framework. It automatically creates mocks, stubs, and assertions to isolate the unit under test and ensure reliable execution. Developers can generate and manage tests using the Test Case Explorer, graphical UI, or guided wizards, no external frameworks required.
Parasoft C/C++test CT (GoogleTest Integration)
C/C++test CT integrates deeply with GoogleTest, enabling teams to write and execute tests using native GoogleTest syntax while benefiting from Parasoft’s enterprise-grade capabilities. Existing GoogleTest assets are fully supported, with advanced parameterization, reporting, and compliance features layered on top.
AI-Driven Test Enhancement
Both solutions now leverage AI agents through the Model Context Protocol (MCP) server. These agents analyze code context directly within the IDE and automatically generate intelligent test scenarios, including edge cases, mocks, and assertions.
Parasoft C/C++test (Proprietary Framework)
C/C++test streamlines unit testing through automated test generation within its robust proprietary framework. It automatically creates mocks, stubs, and assertions to isolate the unit under test and ensure reliable execution. Developers can generate and manage tests using the Test Case Explorer, graphical UI, or guided wizards, no external frameworks required.
Parasoft C/C++test CT (GoogleTest Integration)
C/C++test CT integrates deeply with GoogleTest, enabling teams to write and execute tests using native GoogleTest syntax while benefiting from Parasoft’s enterprise-grade capabilities. Existing GoogleTest assets are fully supported, with advanced parameterization, reporting, and compliance features layered on top.
AI-Driven Test Enhancement
Both solutions now leverage AI agents through the Model Context Protocol (MCP) server. These agents analyze code context directly within the IDE and automatically generate intelligent test scenarios, including edge cases, mocks, and assertions.
5x
Time and cost savings of code reviews.
Decreased
Time spent writing test cases and performing unit and regression tests.
Build, expand, and maintain Java unit and regression test suites faster with AI-enhanced Parasoft Jtest to boost coverage and confidence in every release.
With AI-enhanced Parasoft Jtest, teams can scale Java unit testing and build effective, more maintainable regression suites while accelerating code coverage. Jtest’s proprietary AI automatically generates meaningful JUnit test cases, complete with assertions, to target uncovered lines of code and help teams rapidly expand their coverage.
Within the IDE, Jtest’s Unit Test Assistant helps you expand test cases with AI-driven recommendations to mock or stub dependencies, parameterize, or clone and mutate test cases for greater coverage and resilience.
Teams can also integrate with various LLM providers to refine and refactor test cases. Jtest also safeguards against regression failures by autonomously generating new unit tests directly in CI/CD pipelines to fill coverage gaps without manual effort.
As code evolves, live unit testing in the IDE and test impact analysis integrated into the CI/CD pipeline automatically executes only test cases impacted by code changes to deliver faster feedback and prevent regression failures.
With AI-enhanced Parasoft Jtest, teams can scale Java unit testing and build effective, more maintainable regression suites while accelerating code coverage. Jtest’s proprietary AI automatically generates meaningful JUnit test cases, complete with assertions, to target uncovered lines of code and help teams rapidly expand their coverage.
Within the IDE, Jtest’s Unit Test Assistant helps you expand test cases with AI-driven recommendations to mock or stub dependencies, parameterize, or clone and mutate test cases for greater coverage and resilience.
Teams can also integrate with various LLM providers to refine and refactor test cases. Jtest also safeguards against regression failures by autonomously generating new unit tests directly in CI/CD pipelines to fill coverage gaps without manual effort.
As code evolves, live unit testing in the IDE and test impact analysis integrated into the CI/CD pipeline automatically executes only test cases impacted by code changes to deliver faster feedback and prevent regression failures.
90%
Increase in code coverage.
12%
Increase in developer productivity.
