We present K onure , a new system that uses active learning to infer models of applications that retrieve data from relational databases. K onure comprises a domain-specific language (each model is a program in this language) and associated inference algorithm that infers models of applications whose behavior can be expressed in this language. The inference algorithm generates inputs and database contents, runs the application, then observes the resulting database traffic and outputs to progressively refine its current model hypothesis. Because the technique works with only externally observable inputs, outputs, and database contents, it can infer the behavior of applications written in arbitrary languages using arbitrary coding styles (as long as the behavior of the application is expressible in the domain-specific language). K onure also implements a regenerator that produces a translated Python implementation of the application that systematically includes relevant security and error checks.

@article{ShenR21,
  title = {Active Learning for Inference and Regeneration of Applications that Access Databases},
  author = {Jiasi Shen and Martin C. Rinard},
  journal = {ACM Transactions on Programming Languages and Systems},
  volume = {42},
  number = {4},
  year = {2020},
  doi = {10.1145/3430952}
}