Mon 24 Jun 2019 14:20 - 14:40 at 229AB - Synthesis Chair(s): Nuno P. Lopes

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 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). Konure also implements a regenerator that produces a translated Python implementation of the application that systematically includes relevant security and error checks.

Mon 24 Jun

pldi-2019-papers
14:00 - 15:30: PLDI Research Papers - Synthesis at 229AB
Chair(s): Nuno P. LopesMicrosoft Research
pldi-2019-papers14:00 - 14:20
Talk
Tristan KnothUniversity of California at San Diego, USA, Di WangCarnegie Mellon University, Nadia PolikarpovaUniversity of California, San Diego, Jan HoffmannCarnegie Mellon University
Media Attached
pldi-2019-papers14:20 - 14:40
Talk
Jiasi ShenMassachusetts Institute of Technology, Martin RinardMassachusetts Institute of Technology
DOI Media Attached
pldi-2019-papers14:40 - 15:00
Talk
Yuepeng WangUniversity of Texas at Austin, James DongUniversity of Texas at Austin, USA, Rushi ShahUT Austin, Isil DilligUT Austin
Media Attached
pldi-2019-papers15:00 - 15:20
Talk
Arun IyerMicrosoft Research, India, Manohar JonnalageddaInpher Inc., Switzerland, Suresh ParthasarathyMicrosoft Research, India, Arjun RadhakrishnaMicrosoft, Sriram RajamaniMicrosoft Research
Media Attached