A Recurrent Neural Network Based Patch Recommender for Linux Kernel Bugs

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Bableshwar, A. (2019). A Recurrent Neural Network Based Patch Recommender for Linux Kernel Bugs. Unc Charlotte Electronic Theses And Dissertations.
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Abstract

Software bugs result in a variety of issues including system crashes, loss of system performance, incorrect output and security vulnerabilities. In this thesis, we explore the design of a patch recommender system for the Linux kernel. Software tools that aid the developer in quickly developing bug fixes can help in improving programmer productivity. Previous efforts in automated bug resolution uses a search approach for exploring the automatically generated patch space for functionally correct patches. In contrast, we focus on bug reports, and patch commit descriptions manually generated by humans and expressed in a natural language such as English. Our goal is to relate a new bug description to the most closely related patch that resolved a similar bug in the past. Compared to existing approaches, we do not attempt to generate a patch code, but instead point the user to potential patches that enable developers to identify the part of the code base that they should focus on. Our approach is thus complementary to existing research. We explore the use of Natural Language Processing (NLP) to mine bug/patch descriptions. Our dataset consists of previously resolved bugs and the corresponding patches from the Linux kernel project. We pose the bug-patch matching as a semantic similarity NLP problem. After generating a custom word embedding for the bug-patch dataset, we train a Siamese LSTM network that outputs the Manhattan distance between bug and the patch. The Keras-Tensorflow framework is used. We then evaluate our approach with bugs from the test set, and determine the top-K matches for the bug from all existing patches. At the 50th percentile of the test bugs, the correct patch occurs within top 11.5 patch recommendations output by the model.

Details
Author

Bableshwar, Anusha

Title

A Recurrent Neural Network Based Patch Recommender for Linux Kernel Bugs

Physical Description

1 online resource (40 pages) : PDF

Date

2019

Degree Granting Institution

University of North Carolina at Charlotte

Abstract

Software bugs result in a variety of issues including system crashes, loss of system performance, incorrect output and security vulnerabilities. In this thesis, we explore the design of a patch recommender system for the Linux kernel. Software tools that aid the developer in quickly developing bug fixes can help in improving programmer productivity. Previous efforts in automated bug resolution uses a search approach for exploring the automatically generated patch space for functionally correct patches. In contrast, we focus on bug reports, and patch commit descriptions manually generated by humans and expressed in a natural language such as English. Our goal is to relate a new bug description to the most closely related patch that resolved a similar bug in the past. Compared to existing approaches, we do not attempt to generate a patch code, but instead point the user to potential patches that enable developers to identify the part of the code base that they should focus on. Our approach is thus complementary to existing research. We explore the use of Natural Language Processing (NLP) to mine bug/patch descriptions. Our dataset consists of previously resolved bugs and the corresponding patches from the Linux kernel project. We pose the bug-patch matching as a semantic similarity NLP problem. After generating a custom word embedding for the bug-patch dataset, we train a Siamese LSTM network that outputs the Manhattan distance between bug and the patch. The Keras-Tensorflow framework is used. We then evaluate our approach with bugs from the test set, and determine the top-K matches for the bug from all existing patches. At the 50th percentile of the test bugs, the correct patch occurs within top 11.5 patch recommendations output by the model.

Genre

masters theses

Subjects--Topics

Computer engineering

Degree

M.S.

Keywords

Beautifulsoup
Linux Kernel Bugs
NLP
Patch
RNN
Siamese

Subject Area

Engineering

Advisor(s)

Ravindran, Arun

Committee Members

Tabkhi, Hamed
Willis, Andrew

Degree Note

Thesis (M.S.)--University of North Carolina at Charlotte, 2019.

Rights Statement

This Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s). For additional information, see http://rightsstatements.org/page/InC/1.0/.

Rights Holder Information

Copyright is held by the author unless otherwise indicated.

Identifier

Bableshwar_uncc_0694N_12157

Permalink

http://hdl.handle.net/20.500.13093/etd:2082