Using dynamic analysis to infer Python programs and convert them into database programs
Name
1098214790-MIT.pdf
Size
1.01 MB
Format
Adobe PDF
Checksum (MD5)
2693ae9302e942b278cc50670b42e636
Author(s)
Wu, Jerry.
Advisor(s)
Martin Rinard.
Date Issued
2018
Publisher
Massachusetts Institute of Technology
Abstract
I present Nero, a new system that automatically infers and regenerates programs that access databases. The developer first implements a Python program that uses lists and dictionaries to implement the database functionality. Nero then instruments the Python list and dictionary implementations and uses active learning to generate inputs that enable it to infer the behavior of the program. The program can be implemented in any arbitrary style as long as it implements behavior expressible in the domain specific language that characterizes the behaviors that Nero is designed to infer. The regenerated program replaces the Python lists and dictionaries with database tables and contains all code required to successfully access the databases. Results from several inferred and regenerated applications highlight the ability of Nero to enable developers with no knowledge of database programming to obtain programs that successfully access databases.
Description
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 195-196).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.
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