华东师范大学学报（自然科学版） ›› 2022, Vol. 2022 ›› Issue (4): 43-55.doi: 10.3969/j.issn.1000-5641.2022.04.005

• 计算机科学 • 上一篇    下一篇

金融科技软件自动化测试用例的冗余评价和削减方法

龚鑫1, 徐立华2, 窦亮1,*(), 赵瑞祥3   

  1. 1. 华东师范大学 计算机科学与技术学院, 上海　200062
    2. 上海纽约大学 工程与计算机科学部, 上海　200122
    3. 中汇信息技术(上海)有限公司, 上海　201203
  • 收稿日期:2021-01-07 出版日期:2022-07-25 发布日期:2022-07-19
  • 通讯作者: 窦亮 E-mail:ldou@cs.ecnu.edu.cn
  • 基金资助:
    上海市“科技创新行动计划”高新技术领域项目 (20511102502)

Redundancy measurement and reduction of automated tests in financial technology

Xin GONG1, Lihua XU2, Liang DOU1,*(), Ruixiang ZHAO3   

  1. 1. School of Computer Science and Technology, East China Normal University, Shanghai　200062, China
    2. Engineering and Computer Science Department, New York University Shanghai, Shanghai　200122, China
    3. China Foreign Exchange Trade System (CFETS) Information Technology (Shanghai) Co. Ltd., Shanghai　201203, China
  • Received:2021-01-07 Online:2022-07-25 Published:2022-07-19
  • Contact: Liang DOU E-mail:ldou@cs.ecnu.edu.cn

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摘要：

随着金融科技软件的开发迭代, 软件的复杂度日益提升, 这将会导致测试套件体量逐渐增大, 并出现测试冗余现象. 为了有效地对测试冗余因素进行量化和消解, 提出了一种最佳覆盖项测试冗余评价指标MVI (Most Valuable Item) , 以及一种基于MVI指标的测试用例削减算法MVIR (Most Valuable Item Reduction). 在实际金融科技软件中的实验结果表明, MVIR能够在测试性能损失小于9.20%的前提下, 实现大于89.88%的测试用例削减比例, MVI指标能够有效反映测试套件中的冗余因素大小.

关键词: 金融科技, 软件测试, 测试冗余, 测试用例生成, 测试用例削减

Abstract:

With the development and iteration of financial technology(FinTech) software programs, the size of test suites will gradually increase, which may introduce inherent redundancy. In order to effectively quantify test redundancy, a test redundancy evaluation metric called MVI (Most Valuable Item) is proposed in this study. To verify the validity of the MVI metric, the MVIR (Most Valuable Item Reduction) test case reduction algorithm is proposed. Experimental results show that the MVIR can achieve a test case reduction ratio of more than 89.88% assuming the test performance loss is less than 9.20%, this demonstrates that the MVI metric is valid.

Key words: financial technology, software testing, test redundancy, test generation, test minimization

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