Identify if code has test coverage
using AI
Below is a free classifier to identify if code has test coverage. Just input your text, and our AI will predict if the code has test coverage - in just seconds.
API Access
import nyckel
credentials = nyckel.Credentials("YOUR_CLIENT_ID", "YOUR_CLIENT_SECRET")
nyckel.invoke("if-code-has-test-coverage", "your_text_here", credentials)
fetch('https://www.nyckel.com/v1/functions/if-code-has-test-coverage/invoke', {
method: 'POST',
headers: {
'Authorization': 'Bearer ' + 'YOUR_BEARER_TOKEN',
'Content-Type': 'application/json',
},
body: JSON.stringify(
{"data": "your_text_here"}
)
})
.then(response => response.json())
.then(data => console.log(data));
curl -X POST \
-H "Content-Type: application/json" \
-H "Authorization: Bearer YOUR_BEARER_TOKEN" \
-d '{"data": "your_text_here"}' \
https://www.nyckel.com/v1/functions/if-code-has-test-coverage/invoke
How this classifier works
To start, input the text that you'd like analyzed. Our AI tool will then predict if the code has test coverage.
This pretrained text model uses a Nyckel-created dataset and has 2 labels, including Has Test Coverage and Lacks Test Coverage.
We'll also show a confidence score (the higher the number, the more confident the AI model is around if the code has test coverage).
Whether you're just curious or building if code has test coverage detection into your application, we hope our classifier proves helpful.
Recommended Classifiers
Need to identify if code has test coverage at scale?
Get API or Zapier access to this classifier for free. It's perfect for:
- Quality Assurance Automation: This function can be integrated into continuous integration (CI) pipelines to automatically verify if code changes have adequate test coverage. It ensures that only code meeting coverage criteria is merged, reducing the potential for introducing bugs into production.
- Code Review Enhancement: During code reviews, developers can utilize this function to quickly assess the test coverage of new code submissions. It facilitates more informed discussions during the review process, ensuring that all submitted changes maintain the codebase's quality standards.
- Technical Debt Management: Development teams can use this identifier to spot areas of the codebase lacking test coverage, highlighting technical debt. By addressing these gaps, teams can progressively improve code reliability while reducing the long-term costs associated with bugs.
- Compliance and Audit Reporting: Organizations can leverage this function to ensure compliance with industry standards that mandate a certain level of test coverage. It aids in generating reports for audits, demonstrating due diligence in maintaining code integrity and quality.
- Training and Onboarding New Developers: Incorporating this function into training programs helps new developers understand the importance of test coverage in maintaining software quality. They can receive immediate feedback on their code contributions, promoting best practices from the onset.
- Legacy Code Assessment: This identifier can be particularly useful for evaluating the test coverage of legacy codebases. It allows teams to prioritize which parts of the codebase need refactoring or additional testing, guiding investment in modernizing older systems.
- Performance Metrics for Development Teams: By integrating this function into development performance metrics, teams can track their test coverage trends over time. It creates accountability among developers and promotes a culture of quality ownership within the software development life cycle.