Understanding AI Reasons in Prioritise with AI

AI Reasons explain why a test case was recommended, prioritised, or left out of the current AI prioritisation results.

These reasons are based on signals such as execution history, recent activity, failures, bugs, priority, duration, and test case completeness.

Use AI Reasons as decision support. They help you understand why a test may be important for the current testing goal, but they should be reviewed together with your product knowledge, release scope, and testing strategy.

AI Reasons you may see

Execution activity

These reasons are based on how often a test is executed and how recently it has been used.

Examples:

• “low historical execution frequency”
• “high recent execution frequency”
• “recent execution count”
• “execution regularity”
• “infrequent execution intervals”
• “long gap since last execution”
• “extended period of inactivity”
• “average execution interval is 5 days or more”
• “execution frequency is low”
• “recent execution count is below the expected level”
• “recent execution frequency is below the expected level”

What this means:

The AI is looking at whether the test is used regularly, whether it has been executed recently, and whether it appears to be part of normal testing activity. Tests with very low activity may be less relevant for some prioritisation goals, while frequently executed tests may indicate important regression or business critical coverage.

Failures and stability

These reasons are based on failed runs, recent failures, repeated failures, and unstable outcomes.

Examples:

• “active failure streak”
• “recurring failure streaks”
• “historical failure rate signals”
• “historical failure frequency”
• “high historical failure rate”
• “unstable execution history”
• “unstable execution outcomes”
• “failed within last thirty days”
• “failed within last month”
• “last execution failed”
• “last run failed”
• “recent failure occurrence”
• “recent failure patterns”
• “weighted recent failure trend”
• “poor recovery after failures”
• “consecutive failures reach or exceed threshold”
• “days since last failure exceeds 14 days”

What this means:

The AI is identifying tests that may point to unstable or risky product areas. Recent failures, repeated failures, and inconsistent outcomes can indicate areas that may need closer attention before release.

Bugs and risk signals

These reasons are based on bugs linked to previous executions or the test area.

Examples:

• “high bug density”
• “high bug density per execution”
• “normalised bug density”
• “more than 10 bugs in the last 10 executions”
• “total bugs count exceeds 30”
• “bugs per execution is above the selected threshold”

What this means:

The AI is looking at whether this test or test area has historically been associated with many bugs. A high number of bugs can indicate higher product risk and may make the test more important to review or execute.

Priority and smoke testing relevance

These reasons are based on the priority assigned to the test case.

Examples:

• “based on native priority”
• “prioritizes critical and high priority cases for smoke scope”
• “test belongs to the top 3 priority levels”

What this means:

The AI is taking the test case priority into account. Critical or high priority tests may be more relevant when building focused test runs, smoke tests, or risk based testing scopes.

Duration and test effort

These reasons are based on estimated execution time.

Examples:

• “short estimated duration”
• “penalty for long duration”
• “complex and time consuming procedures”
• “estimated duration is 60 minutes”
• “test is classified as short”

What this means:

The AI is considering how much effort the test may take to execute. Shorter tests may be useful for fast feedback cycles, while longer tests may be deprioritised or left out depending on the selected goal.

Problematic statuses

These reasons are based on execution statuses that may indicate risk or require attention.

Examples:

• “latest run matches problematic status”
• “frequent problematic status history”

What this means:

The AI is looking at whether the test has recently ended in a status that may need attention, or whether problematic statuses appear often in its execution history.

Reasons that start with “out of scope because”

Some AI Reasons explain why a test was not included in the current result set.

Examples:

• “out of scope because execution frequency is low”
• “out of scope because recent execution count is below the expected level”
• “out of scope because estimated duration is 60 minutes”
• “out of scope because test has empty fields or incomplete metadata”
• “out of scope because test is untested”

What this means:

The test did not match the current prioritisation goal or selected criteria. This does not always mean the test is unimportant. It means the test was not considered a good fit for the current AI recommendation context.