应对电商搜索中的漏召回问题：一种基于CoT提示的测试方法
Combating Missed Recalls in E-commerce Search: A CoT-Prompting Testing Approach
AI总结：
针对电商搜索中难以检测的漏召回缺陷问题，提出首个自动化测试方法mrDetector，利用CoT提示驱动LLM生成用户对齐查询，并通过蜕变关系提供测试预言机，在真实工业数据中发现了超百个漏召回且误报率极低。
摘要：
电商应用中的搜索组件通常是复杂的基于AI的系统，容易出现导致漏召回的缺陷——即本应出现在搜索结果中的商品未被列出。这会令店主沮丧并损害应用的盈利能力。然而，由于难以生成与用户对齐的测试用例以及缺乏测试预言机，测试漏召回具有挑战性。在本文中，我们提出了mrDetector，这是首个专门针对漏召回的自动化测试方法。为了解决测试用例生成难题，我们利用用户在搜索过程中构建查询的方式的研究结果，创建了一个CoT提示，通过LLM生成与用户对齐的查询。此外，我们借鉴用户为一家店铺创建多个查询并比较搜索结果的做法，通过蜕变关系提供了测试预言机。使用开放访问数据的大量实验表明，mrDetector优于所有基线，具有最低的误报率。使用真实工业数据的实验表明，mrDetector发现了超过一百个漏召回，仅有17个误报。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
