一句话：提出面向金融科技系统的黑盒测试方法，结合输入类别变异生成测试与优先级差分测试预言，提升分支覆盖并发现故障。
金融科技系统仍严重依赖人工测试，成为发展瓶颈。论文提出 FACTS，一种带工具支持的自动化黑盒测试方法，兼顾测试生成与测试预言。测试生成先从业务逻辑规约提取输入类别，再用系统日志中的真实数据按类别随机取值进行变异。测试预言采用优先级差分测试：将测试输入在最新 head 版本上的执行结果，分别与上一遗留版本（针对未部署新服务）、以及当前部署版本和上一遗留版本（针对已部署既有服务）比较；后者优先检查当前部署版本的行为不一致。应用于中国外汇交易中心 CFETS 平台的 CSTP 子系统，实验显示分支覆盖率提升约 40%，并能高效发现常见故障。
FinTech, short for ``financial technology,'' has advanced the process of transforming financial business from a traditional manual-process-driven to an automation-driven model by providing various software platforms. However, the current FinTech-industry still heavily depends on manual testing, which becomes the bottleneck of FinTech industry development. To automate the testing process, we propose an approach of black-box testing for a FinTech system with effective tool support for both test generation and test oracles. For test generation, we first extract input categories from business-logic specifications, and then mutate real data collected from system logs with values randomly picked from each extracted input category. For test oracles, we propose a new technique of priority differential testing where we evaluate execution results of system-test inputs on the system's head (i.e., latest) version in the version repository (1) against the last legacy version in the version repository (only when the executed test inputs are on new, not-yet-deployed services) and (2) against both the currently-deployed version and the last legacy version (only when the test inputs are on existing, deployed services). When we rank the behavior-inconsistency results for developers to inspect, for the latter case, we give the currently-deployed version as a higher-priority source of behavior to check. We apply our approach to the CSTP subsystem, one of the largest data processing and forwarding modules of the China Foreign Exchange Trade System (CFETS) platform, whose annual total transaction volume reaches 150 trillion US dollars. Extensive experimental results show that our approach can substantially boost the branch coverage by approximately 40%, and is also efficient to identify common faults in the FinTech system.
DOI 原文 ·
@inproceedings{paperbot4667,
title = {FACTS: automated black-box testing of FinTech systems},
author = {Qingshun Wang and Lintao Gu and Minhui Xue and Lihua Xu and Wenyu Niu and Liang Dou and Liang He and Tao Xie},
booktitle = {FSE},
year = {2018},
doi = {10.1145/3236024.3275533}
}