DCLINK: Bridging Data Constraint Changes and Implementations in FinTech Systems

A FinTech system is a cluster of FinTech applications that intensively interact with databases containing a large quantity of user data. To ensure data consistency, it is a common practice to specify data constraints to validate data at runtime. However, data constraints often evolve according to changes in business requirements. Meanwhile, the developers can hardly keep up with the latest requirements during the development cycle. Such an information barrier increases the communication burden and prevents FinTech applications from being updated in time, impeding the development cycle significantly. In this paper, we present a comprehensive empirical study on data constraints in FinTech systems, investigating how they evolve and affect the development process. Our results show that developers find it hard to update their code timely because no mapping from data constraint changes to code is provided. Inspired by the findings from code updates respecting data constraint changes, we propose DCLINK, a traceability link analysis for linking each data constraint change to target methods demanding the code update in the FinTech application. We extensively evaluate DCLINK upon real-world change cases in Ant Group. The results show that DCLINK can effectively and efficiently localize the target methods.