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.

中文翻译：

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用于推断和重新生成访问数据库的应用程序的主动学习

我们提出 K劳力，一个使用主动学习来推断从关系数据库中检索数据的应用程序模型的新系统。ķ劳力包括特定领域的语言（每个模型都是该语言的程序）和相关的推理算法，用于推断应用程序的模型，其行为可以用这种语言表达。推理算法生成输入和数据库内容，运行应用程序，然后观察生成的数据库流量和输出，以逐步完善其当前模型假设。由于该技术仅适用于外部可观察的输入、输出和数据库内容，因此它可以使用任意编码样式推断以任意语言编写的应用程序的行为（只要应用程序的行为可以用特定领域的语言表达）。ķ劳力还实现了一个再生器，它生成应用程序的翻译 Python 实现，系统地包括相关的安全和错误检查。