1 research outputs found
Verifying Data Constraint Equivalence in FinTech Systems
Data constraints are widely used in FinTech systems for monitoring data
consistency and diagnosing anomalous data manipulations. However, many
equivalent data constraints are created redundantly during the development
cycle, slowing down the FinTech systems and causing unnecessary alerts. We
present EqDAC, an efficient decision procedure to determine the data constraint
equivalence. We first propose the symbolic representation for semantic encoding
and then introduce two light-weighted analyses to refute and prove the
equivalence, respectively, which are proved to achieve in polynomial time. We
evaluate EqDAC upon 30,801 data constraints in a FinTech system. It is shown
that EqDAC detects 11,538 equivalent data constraints in three hours. It also
supports efficient equivalence searching with an average time cost of 1.22
seconds, enabling the system to check new data constraints upon submission.Comment: 14 pages, 11 figures, accepted by ICSE 202