TY - GEN
T1 - FinHunter
T2 - 32nd ACM International Conference on the Foundations of Software Engineering, FSE Companion
AU - Ding, Xuanwen
AU - Wang, Qingshun
AU - Liu, Dan
AU - Xu, Lihua
AU - Xiao, Jun
AU - Zhang, Bojun
AU - Li, Xue
AU - Dou, Liang
AU - He, Liang
AU - Xie, Tao
N1 - Publisher Copyright:
© 2024 Copyright held by the owner/author(s).
PY - 2024/7/10
Y1 - 2024/7/10
N2 - Ensuring high quality of software systems is highly critical in mission-critical industrial sectors such as FinTech. To test such systems, replaying the historical data (typically in the form of input field values) recorded during system real usage has been quite valuable in industrial practices; augmenting the recorded data by crossing over and mutating them (as seed inputs) can further improve the structural coverage achieved by testing. However, the existing augmentation approaches based on search-based test generation face three major challenges: (1) the recorded data used as seed inputs for search-based test generation are often insufficient for achieving high structural coverage, (2) randomly crossing over individual primitive field values easily breaks the input constraints (which are often not documented) among multiple related fields, leading to invalid test inputs, and (3) randomly crossing over constituent primitive fields within a composite field easily breaks the input constraints (which are often not documented) among these constituent primitive fields, leading to invalid test inputs. To address these challenges, in this paper, we propose FinHunter, a search-based test generation framework that improves a genetic algorithm for structural testing. FinHunter includes the technique of gene-pool expansion to address the insufficient seeds for search-based test generation, and the technique of multi-level crossover to address input-constraint violations during crossover. We apply FinHunter in the Ant Group to test a real commercial system, with more than 100,000 lines of code, and 46 different interfaces each of which corresponds to a service in the system. The system provides a range of services, including customer application processing, analysis, appraisal, credit extension decision-making, and implementation. Our experimental results show that FinHunter outperforms the current practice in the Ant Group and the traditional genetic algorithm.
AB - Ensuring high quality of software systems is highly critical in mission-critical industrial sectors such as FinTech. To test such systems, replaying the historical data (typically in the form of input field values) recorded during system real usage has been quite valuable in industrial practices; augmenting the recorded data by crossing over and mutating them (as seed inputs) can further improve the structural coverage achieved by testing. However, the existing augmentation approaches based on search-based test generation face three major challenges: (1) the recorded data used as seed inputs for search-based test generation are often insufficient for achieving high structural coverage, (2) randomly crossing over individual primitive field values easily breaks the input constraints (which are often not documented) among multiple related fields, leading to invalid test inputs, and (3) randomly crossing over constituent primitive fields within a composite field easily breaks the input constraints (which are often not documented) among these constituent primitive fields, leading to invalid test inputs. To address these challenges, in this paper, we propose FinHunter, a search-based test generation framework that improves a genetic algorithm for structural testing. FinHunter includes the technique of gene-pool expansion to address the insufficient seeds for search-based test generation, and the technique of multi-level crossover to address input-constraint violations during crossover. We apply FinHunter in the Ant Group to test a real commercial system, with more than 100,000 lines of code, and 46 different interfaces each of which corresponds to a service in the system. The system provides a range of services, including customer application processing, analysis, appraisal, credit extension decision-making, and implementation. Our experimental results show that FinHunter outperforms the current practice in the Ant Group and the traditional genetic algorithm.
KW - FinTech
KW - automated test generation
KW - genetic algorithm
UR - https://www.scopus.com/pages/publications/85199077549
U2 - 10.1145/3663529.3663823
DO - 10.1145/3663529.3663823
M3 - 会议稿件
AN - SCOPUS:85199077549
T3 - FSE Companion - Companion Proceedings of the 32nd ACM International Conference on the Foundations of Software Engineering
SP - 10
EP - 20
BT - FSE Companion - Companion Proceedings of the 32nd ACM International Conference on the Foundations of Software Engineering
A2 - d�Amorim, Marcelo
PB - Association for Computing Machinery, Inc
Y2 - 15 July 2024 through 19 July 2024
ER -