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.
@article{arxiv.2406.19633,
title = {Combating Missed Recalls in E-commerce Search: A CoT-Prompting Testing Approach},
author = {Shengnan Wu and Yongxiang Hu and Yingchuan Wang and Jiazhen Gu and Jin Meng and Liujie Fan and Zhongshi Luan and Xin Wang and Yangfan Zhou},
journal= {arXiv preprint arXiv:2406.19633},
year = {2024}
}
Comments
Companion Proceedings of the 32nd ACM International Conference on the Foundations of Software Engineering (FSE Companion '24), July 15--19, 2024, Porto de Galinhas, Brazil