Introducing industry-first Agentic AI to virtualize services. In natural language. Learn More >>
Our AI capabilities support testing from code to release. Here’s where it’s working today.
Optimize static analysis workflows, streamline code compliance, and accelerate remediation of static analysis findings with AI-enhanced solutions.
Jump to: Static Analysis
Generate Java tests in bulk for existing legacy code or for new code with AI-enabled unit test creation to rapidly reach high code coverage metrics.
Jump to: Unit Testing
Leverage AI to scriptlessly create automated, effective, scalable API test scenarios from manual actions in the UI, recorded traffic, or service definitions.
Jump to: API Testing
Leverage ML to self-heal Selenium tests during execution and receive guidance in the IDE environment to fix them automatically.
Jump to: UI Testing
Utilize test impact analysis (TIA) to easily identify which tests to rerun when code changes and get faster feedback.
Jump to: Regression Testing
Generate virtual services by chatting with our agentic AI assistant in plain language, no coding needed. Accelerate test environment creation and move forward without the bottlenecks.
Jump to: Service Virtualization
“Parasoft doubles down on infusing AI capabilities into its platform. It has undisputed strengths in API testing made easy with AI and integrated with its service virtualization offering. Shift-left performance testing for converged functional and performance testing and its long-time mature analytical reporting are also strong features….
“Parasoft can rave about its ‘built here, not acquired’ product and innovation approach, which strengthens a consistent experience across all testing types.”
Diego Lo Giudice, Forrester Vice President and Principal Analyst
A common roadblock to adopting static analysis tools successfully is managing a large number of warnings and handling perceived false positives. Whatever the compliance requirements—MISRA, CWE, OWASP, and more—our automated static analysis tools enhanced with AI and ML flag and prioritize the rule violations that the team needs to fix first.
A hotspot detection engine works with an advanced AI-based model to assign violations to developers matching their best skills and experience—learning from violations they fixed in the past.
Our patented AI and ML enhanced static analysis solutions offer the following benefits:
Our static analysis solutions enhanced with AI assist developers to triage and prioritize the number of violations so they can focus on higher priority issues.
21-28%
Drop in developers’ average amount of time required to fix or suppress a problem.
23%
Average reduction of time required to fix a single violation for the entire team.
Java development teams can use Parasoft Jtest enhanced with AI to create high-quality unit tests and increase code coverage with the following capabilities:
100%
Acceleration in unit test generation.
90%
Reduction of test execution time in the CI/CD pipeline.
Move quickly from intent to implementation by using the chat interface embedded directly in the SOAtest UI.
The AI assistant leverages LLM integration—whether cloud-based or local—to interpret API service definitions and natural language instructions. It can guide you step-by-step or generate complete, parameterized test scenarios with meaningful test data, all with a simple conversation.
In addition to agentic AI, teams can automate test creation from real-world interactions using the SOAtest Smart API Test Generator. Record REST API traffic triggered through manual UI interactions or automated test executions by using the Parasoft Recorder or by deploying a proxy between integrated services. Then import those traffic files into SOAtest to automatically generate codeless API test scenarios.
SOAtest’s AI analyzes traffic patterns, builds test flows, and dynamically extracts data from responses to apply to downstream requests. It also autoconfigures assertions to ensure meaningful validations. Machine learning refines this process over time by learning from your existing test suite and customized templates.
Testing AI-driven applications requires new approaches to handle their dynamic non-deterministic behavior. Parasoft now includes powerful capabilities built for this challenge.
The new AI Assertor and AI Data Bank let testers describe complex dynamic validation logic and data extraction in natural language—, eliminating the need for hard-coded validation logic. These tools are ideal for validating variable AI outputs and streamlining test authoring.
You also get support for testing Model Context Protocol (MCP) servers. This lets you test the tools that AI agents depend on, all via the codeless SOAtest UI.
Sabre turned to AI-powered automated test case generation and execution as a primary goal to deliver quality services.
67%
Reduced the time and effort to certify a new service by 67%.
$720k
Saved annually with productivity gains.
Three common Selenium testing challenges application teams experience include:
Development teams efficiently achieve the following with Parasoft Selenic enhanced with AI/ML:
Prior to Caesars automating testing with AI-optimized Parasoft Selenic, executing UI tests took excessively long—many days.
96%
Improvement in UI testing by moving from manual to automation.
TIA’s AI leverages code coverage analysis to correlate recent code changes to impacted test cases, focusing testing on validating application changes. Here’s how TIA is implemented across the software development lifecycle:
“Now we run regression tests across everything, so we might catch something we didn’t before…that is where our quality has really gone up. “…automated coverage and ongoing regression testing has definitely helped a lot with efficiency.”
Heath McIntyre, Director of Software Development, CAPITAL Services
Embedded directly within the Virtualize UI, this chat-based assistant uses LLM-powered reasoning to interpret natural language instructions. Describe what you need—such as a service that returns specific data patterns or simulates an unavailable dependency—and generate fully configured virtual services from API service definitions, sample request/response pairs, or a written description of the service.
The AI Assistant handles complex setup tasks like parameterizing responses with input data and configuring sensible default values. It significantly reduces the expertise required, aligning well with API-first workflows for earlier, more efficient testing—even when real systems are unavailable.
In addition, Virtualize now enables the testing of AI-infused applications that use Model Context Protocol (MCP), making it possible to simulate and control the behavior of dependent MCP servers when testing generative AI agents. As MCP adoption grows, this capability positions teams to validate next-generation intelligent systems with confidence.
