Using Active Learning to Synthesize Models of Applications That Access Databases
Speaker
Jiasi Shen，Massachusetts Institute of Technology
Time
2019-12-04 14:30:00 ~ 2019-12-04 16:00:00
Location
Room 1319, Software Expert Building
Host
Qinxiang Cao, Assistant Professor, John Hopcroft Center for Computer Science
Abstract
We present Konure, a new system that uses active learning to infer models of applications that access relational databases. Konure 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 configurations, 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 configurations, 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). Konure also implements a regenerator that produces a translated Python implementation of the application that systematically includes relevant security and error checks.
Bio
Jiasi Shen is a PhD student at MIT advised by professor Martin Rinard. She received her bachelor's degree in Computer Science from Peking University. Her research interests are in programming languages and software engineering.