  Search components in e-commerce apps, often complex AI-based systems, are
prone to bugs that can lead to missed recalls - situations where items that
should be listed in search results aren't. This can frustrate shop owners and
harm the app's profitability. However, testing for missed recalls is
challenging due to difficulties in generating user-aligned test cases and the
absence of oracles. In this paper, we introduce mrDetector, the first automatic
testing approach specifically for missed recalls. To tackle the test case
generation challenge, we use findings from how users construct queries during
searching to create a CoT prompt to generate user-aligned queries by LLM. In
addition, we learn from users who create multiple queries for one shop and
compare search results, and provide a test oracle through a metamorphic
relation. Extensive experiments using open access data demonstrate that
mrDetector outperforms all baselines with the lowest false positive ratio.
Experiments with real industrial data show that mrDetector discovers over one
hundred missed recalls with only 17 false positives.
