A suite of automated test cases was run multiple times on the same release of the SUT in the same test environment. Consider analyzing a test histogram that shows the distribution of test results (pass, fail, etc.) for each test case across these runs. Which of the following potential issues is MOST likely to be identified as a result of such an analysis?
Correct Answer: C
TAE recommends monitoring test results over repeated executions to detect non-determinism and flakiness. A histogram showing pass/fail distributions per test across multiple runs in the same environment and on the same SUT version is especially useful for identifying tests whose outcomes vary without corresponding changes. If a test sometimes passes and sometimes fails under equivalent conditions, the distribution reveals instability: repeated failures for the same test, intermittent patterns, or inconsistent outcomes compared with other tests that remain stable. This is a classic indicator of flaky tests or unstable test design (e.g., synchronization issues, hidden dependencies, data leakage, timing sensitivity) and is a key maintainability /reliability concern in automation programs. While execution time outliers (A) require time-series or duration metrics rather than pass/fail distributions, a result histogram primarily focuses on outcome variability, not performance. Security vulnerabilities (B) are not identifiable from outcome distributions; they require static analysis, code review, or security testing methods. Maintainability issues (D) are generally inferred from code structure metrics (complexity, duplication), change frequency, or effort trends, not from pass/fail distributions across runs. Therefore, the most likely issue identified by analyzing such a histogram is unstable automated test cases.
