Many programs that interact with a database need to undergo schema refactoring several times during their life cycle. Since this process typically requires making significant changes to the program's implementation, schema refactoring is often non-trivial and error-prone. Motivated by this problem, we propose a new technique for automatically synthesizing a new version of a database program given its original version and the source and target schemas. Our method does not require manual user guidance and ensures that the synthesized program is equivalent to the original one. Furthermore, our method is quite efficient and can synthesize new versions of database programs (containing up to 263 functions) that are extracted from real-world web applications with an average synthesis time of 69.4 seconds.
Mon 24 JunDisplayed time zone: Tijuana, Baja California change
14:00 - 15:30 | |||
14:00 20mTalk | Resource-Guided Program Synthesis PLDI Research Papers Tristan Knoth University of California at San Diego, USA, Di Wang Carnegie Mellon University, Nadia Polikarpova University of California, San Diego, Jan Hoffmann Carnegie Mellon University Media Attached | ||
14:20 20mTalk | Using Active Learning to Synthesize Models of Applications That Access Databases PLDI Research Papers Jiasi Shen Massachusetts Institute of Technology, Martin C. Rinard Massachusetts Institute of Technology DOI Media Attached | ||
14:40 20mTalk | Synthesizing Database Programs for Schema Refactoring PLDI Research Papers Yuepeng Wang University of Texas at Austin, James Dong University of Texas at Austin, USA, Rushi Shah UT Austin, Isil Dillig UT Austin Media Attached | ||
15:00 20mTalk | Synthesis and Machine Learning for Heterogeneous Extraction PLDI Research Papers Arun Iyer Microsoft Research, India, Manohar Jonnalagedda Inpher Inc., Switzerland, Suresh Parthasarathy Microsoft Research, India, Arjun Radhakrishna Microsoft, Sriram Rajamani Microsoft Research Media Attached | ||