Sembi, a leading SaaS portfolio of testing and security solutions, announced AI Test Prioritization for its leading test management brands. This new feature joins the portfolio of AI-powered process improvement already available in TestRail, the leading purpose-built QA test management platform, and Xray, Jira’s leading test management solution, to help quality professionals identify high-risk tests faster using AI-powered prioritization based on historical risks, defect patterns, and semantic context.
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“Over the last year, Sembi has introduced AI capabilities that help teams create test cases, accelerate automation, and evaluate AI systems. We are now prioritizing execution and perfecting test workflows,” said Judy Bossi, VP of Products at Sembi.
According to Sembi’s Software Quality Pulse Report, only 17% of engineering teams feel their testing has improved as the amount of code generated by AI increases. Teams waste valuable time manually deciding what to test first, while critical defects can remain buried among hundreds of AI-driven test cases.
Unlike traditional prioritization tools that rely primarily on past failures, TestRail 10.5 and Xray 15.0 combine machine learning and AI-driven semantic analysis to identify high-risk tests even when little or no execution history exists.
“Traditional prioritization models can make it difficult to identify risks when little or no execution data is available,” said Annie O’Brien, director of product management at Xray. “By combining historical risk signals with AI-driven semantic analysis, teams can look beyond historical patterns to identify the tests most likely to find defects, allowing them to focus on what matters most.”
AI test prioritization, integrated across Sembi’s test portfolio, leverages customized machine learning to guide engineers which tests to prioritize based on their individual goals.
- Automatically prioritize high-risk tests: We score every test based on analysis of execution history from 7 to 180 days, eliminating the need to manually review execution logs and providing actionable data.
- Test rankings with AI-driven semantic analysis: Create more meaningful filled orders with specific labels (compliance, security, performance) and historical risk signals.
- Recommendations for transparent AI: Use AI-generated values and inferences to prioritize tests and give your team confidence in their test plans.
For Xray customers, AI Test Prioritization brings intelligent risk-based testing directly into Jira-native workflows, helping agile and DevOps teams prioritize tests in the same environment they manage requirements, issues, and releases. TestRail customers support a dedicated QA team to manage structured test repositories and complex test suites, allowing them to focus on running the tests that are most likely to uncover risks.
To support customer control and data governance, both TestRail and Xray provide administrative controls for managing AI features, including the ability to disable AI features at the instance, user, or project level. Customer data is not used to train, fine-tune, or improve publicly available AI models. All data processed by AI Services is securely processed, encrypted in transit and at rest, and not permanently stored beyond the processing necessary to provide the requested functionality.
“AI test prioritization represents the next step in the AI-powered testing workflows we are building across our testing solutions,” said Judy Bossi, vice president of products at Sembi. “Over the last year, we’ve introduced AI capabilities to help teams create test cases, accelerate automation, evaluate AI systems, and prioritize execution. As agent orchestration and MCP begin to reshape what’s possible across the software quality lifecycle, we’re excited about the potential to create even more connected and intelligent quality experiences in the future.”
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