Automatic patch generation via learning from successful human patches
Name
1036987605-MIT.pdf
Description
Full printable version
Size
25.12 MB
Format
Adobe PDF
Checksum (MD5)
f1ae9ef3c2695e7d443d229df1841bad
Author(s)
Long, Fan, Ph. D. Massachusetts Institute of Technology
Advisor(s)
Martin C. Rinard.
Date Issued
2018
Publisher
Massachusetts Institute of Technology
Abstract
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.
Description
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 285-296).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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