Transforming Java Code Testing: Accelerating Success With AI

When AI is employed in code-level testing, including static analysis processes and unit testing, it provides developers with a multitude of advantages to enhance and expedite their quality assurance and testing efforts. From the viewpoint of management, these enhancements result in heightened efficiency in the development of new code and assist projects in adhering to their schedules and budget constraints for software deliveries.

Watch to learn about how solutions that leverage AI optimize and accelerate Java code testing practices and triage and remediate static analysis findings.

Key Takeaways

• AI optimizes static analysis triage to speed up violation fixes.
• AI improves unit testing from creation to maintenance and execution.
• AI increases developer productivity, helping projects stay on schedule and budget.

Optimizing Static Analysis With AI

Static analysis is a widely adopted practice for ensuring code quality and security. It’s beneficial for catching defects early in the development lifecycle, is easy to implement, and integrates well into developer IDEs or CI/CD pipelines. However, even with these benefits, challenges remain.

Challenges in Static Analysis Adoption

• Static Analysis Noise: Large, complex codebases can generate thousands of findings, making it hard to prioritize which violations are critical and which are just noise. AI can help teams more easily identify and prioritize violations.
• Compliance Requirements: Meeting specific coding guidelines like OWASP or CWE requires specialized knowledge. While tools identify defects, developers may lack the expertise to fix them efficiently, especially those related to compliance standards.
• Time for Remediation: Even with documentation and suggested fixes, developers must shift focus from new code development to analyzing rules and applying fixes within their codebase. This takes time and impacts productivity.

How AI Addresses These Challenges

Parasoft utilizes AI to optimize static analysis workflows. Their DTP platform analyzes static analysis results and testing metrics. ML-based widgets display classification results based on past user actions, allowing the AI to learn from triage decisions and recommend prioritization for new findings. This significantly reduces the workload, often from thousands of violations down to a manageable number that require prioritization.

AI also analyzes the root cause of violations, grouping related issues. This allows managers to assign clusters of violations to a single developer, reducing work duplication. Furthermore, AI can analyze past triage actions to recommend assigning violations to specific developers based on their past remediation history.

For actual fixes, Parasoft’s generative AI can quickly remediate violations directly within the IDE, helping developers get back to writing new code faster.

Accelerating Unit Testing With AI

Unit testing is vital for software quality, but it comes with its own set of challenges that can make initiatives unsuccessful.

Barriers to Successful Unit Testing

• Time-Consuming: Developers often prefer writing new code over creating and maintaining test cases. Complex code requires more time to test, and ensuring tests are isolated from external dependencies (like databases or services) through mocking and stubbing is technically demanding and time-consuming.
• Test Maintenance: Code changes require test modifications, leading to failures that need maintenance. Keeping regression testing suites clean is crucial but tedious.
• Code Coverage Targets: Achieving high code coverage, often a goal of 80%, means testing all code paths and edge cases, which can be daunting and consume significant engineering hours.
• Legacy Code: Older codebases may not be written with maintainability or modern quality expectations in mind, making unit test creation more challenging and time-consuming.
• Resistance to Testing: The time investment in testing versus new code development can lead to a trade-off, potentially increasing the risk of bugs in production.

AI’s Role in Streamlining Unit Testing

Parasoft Jtest offers AI-powered automated and guided workflows to overcome these barriers. It enables teams to quickly generate bulk unit tests for uncovered code, rapidly increasing coverage. Developers can then use AI-assisted test creation to augment existing tests, generating mocks, stubs, and assertions, or identifying which tests to clone or modify for better coverage.

New generative AI capabilities allow developers to instruct the AI via natural language prompts to refactor test cases in specific ways, offering immense flexibility. Compared to using general LLMs alone, Jtest provides more consistent and higher-quality test generation, is scalable with bulk creation, and can be an on-premise solution, addressing company policies against SaaS tools.

Validating Changes Faster with Test Impact Analysis

When validating code changes, especially in pull requests, waiting for feedback can cause delays. Parasoft’s AI-powered Test Impact Analysis (TIA) provides immediate feedback by identifying and running only the test cases impacted by code modifications. This significantly speeds up the feedback loop and reduces the strain on DevOps infrastructure by avoiding the need to run complete test suites for every change.

TIA works by analyzing the test suite to understand which code each test covers, then analyzing which code has changed. It then identifies the specific test cases that need to be run, focusing efforts and saving time and resources. This is particularly effective in CI/CD pipelines for pull requests, allowing for much faster validation of changes.