Automatic patch generation via learning from successful human patches

Automatic patch generation holds out the promise of automatically correcting software defects without the need for developers to manually diagnose, understand, and correct these defects. This dissertation presents two novel patch generation systems, Prophet and Genesis, which learn from past successful human patches to enhance the patch generation process. The core of Prophet and Genesis is a novel learning technique that extracts universal properties of correct code and a novel inference technique that generalizes universal patching strategies across different applications. The results show that the learning and inference techniques enable Prophet and Genesis to operate with rich and tractable search spaces that contain many useful patches and efficient search algorithms that prioritize correct patches. By collectively leveraging development efforts worldwide, Prophet and Genesis automatically generate correct patches for real-world defects in large open-source C and Java applications with up to millions lines of code.

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2018

Author dc:creator

• Long, Fan, Ph. D. Massachusetts Institute of Technology

Advisor dc:contributor.advisor

• Martin C. Rinard.

• Electrical Engineering and Computer Science.

Statement dc:rights

• 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.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/115774

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/115774