jtest-static-analysis and jtest-unit-testing Skills Demo (Windows, Linux, macOS)

This tutorial shows how to integrate the jtest-static-analysis and jtest-unit-testing skills into a coding agent, then use them to fix static analysis violations and generate unit tests in an example project.

Skill sources are located in repository: parasoft/jtest-ai-agent-skills

For the purpose of this tutorial we demonstrate integration with GitHub Copilot CLI agent, but the skill can be used with any coding agent that supports SKILL.md skills and has access to Jtest MCP tools.

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Overview

This repository is an example java project that demonstrates how the Parasoft Jtest AI agent skills can be used to automatically fix static analysis violations inside a GitHub CI/CD pipeline.

The key idea is that when a pull request is created, the pipeline runs Jtest, detects violations, and then invokes an AI coding agent (GitGub Copilot, or others) equipped with Jtest skills and MCP tools to automatically fix the violations, commit the fixes to a new branch, and open a new pull request.

The Jtest AI Agent skills used in this project are taken from the parasoft/jtest-ai-agent-skills repository.

Prerequisites

The following prerequisites apply to both the GitHub Actions and local workstation workflows.

• Install and license Parasoft Jtest
  Parasoft Jtest 2026.1 or later must be installed and licensed on the machine (or runner) where analysis will execute. Note the installation directory — it is required for the JTEST_HOME setting.

• Install and configure GitHub Copilot CLI
  The GitHub Copilot CLI agent must be installed and authenticated. Refer to the GitHub Copilot CLI documentation for installation steps. When selecting a model, choose one capable of multi-step reasoning and tool use.

• Integrating Jtest AI Solutions with GitHub Copilot CLI
  Clone repository parasoft/jtest-ai-agent-skills and perform according parasoft/jtest-ai-agent-skills#integrating-jtest-ai-solutions-with-your-coding-agent using install.sh script to configure jtest MCP and add the skills to your agent's skill set.

GitHub Action workflow

1. Preparation

1. Fork or copy this repository.
2. Set up and connect a GitHub self-hosted runner: Settings > Actions > Runners. See also Prerequisites above.
3. Enable the following option in your repository: Settings > Actions > General > Allow GitHub Actions to create and approve pull requests.
4. Clone the repository to your local machine.
5. Configure JTEST_HOME and other settings in .github/workflows/jtest-analyzer.config or via environment variables as described in parasoft/jtest-ai-agent-skills#configuration-reference

2. Introducing a Code Change

1. Create a feature branch in the local repository.
2. Introduce a change to the source code.
3. Push the branch to the remote GitHub repository.
4. Open a pull request on GitHub to merge your feature branch into your default branch (master or main).

3. CI/CD Pipeline Execution

Opening a pull request triggers the workflow defined in .github/workflows/jtest-ai-agent-workflow.yml:

1. The project is built.
2. Jtest runs static analysis by running jtest-analyze.sh and archives the reports as build artifacts.
3. If violations are found, the AI Autofix step is triggered:

• A new autofix/pr-<number>/<timestamp> branch is created.
• The AI agent processes violations rule by rule.
• Each fix is verified by running build-verify.sh.
• Verified fixes are committed to the autofix branch (one commit per rule).
• The autofix branch is pushed to the remote repository.
• A pull request from autofix into your feature branch is opened, and a comment with links is posted on the original PR.

4. Reviewing the Autofix Pull Request

1. Review the changes proposed in the autofix branch.
2. Merge the autofix branch into your feature branch.
3. Verify that the pipeline passes on the updated feature branch.
4. Merge the feature branch into your default branch (master or main).

Local workstation workflow

1. Preparation

1. Clone the repository to your local machine.
2. Configure JTEST_HOME and other settings in .github/workflows/jtest-analyzer.config or via environment variables as described in parasoft/jtest-ai-agent-skills#configuration-reference
   a) You may omit ANALYZED_PROJECT_PATH since it is set automatically in .github/workflow/jtest-ai-agent-run.sh script
   b) Ensure that JTEST_COMMIT_FIXES is set to false

2. Manually execute the workflow

1. Run script .github/workflow/jtest-ai-agent-run.sh

3. Review changes

1. Review the changes applied on project code.