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The role of dual slicing in automatic program repair Sintaha, Mifta

Abstract

Contextual information plays a vital role for software developers when understanding and fixing a bug. Context can also be important in deep learning-based program repair to provide extra information about the bug and its fix. Existing techniques, however, treat context in an arbitrary manner, by extracting code in close proximity of the buggy statement within the enclosing file, class, or method, without any analysis to find actual relations with the bug. To reduce noise, they use a predefined maximum limit on the number of tokens to be used as context. We present a program slicing-based approach, in which instead of arbitrarily including code as context, we analyze statements that have a control or data dependency on the buggy statement. We propose a novel concept called dual slicing, which leverages the context of both buggy and fixed versions of the code to capture relevant repair ingredients. We present our technique and tool called KATANA, the first to apply slicing-based context for a program repair task. The results show KATANA effectively preserves sufficient information for a model to choose contextual information while reducing noise. We compare against four recent state-of-the-art context-aware program repair techniques. Our results show KATANA fixes between 1.5 to 3.7 times more bugs than existing techniques.

Item Metadata

Title
The role of dual slicing in automatic program repair
Creator
Sintaha, Mifta
Supervisor
Mesbah, Ali
Publisher
University of British Columbia
Date Issued
2022
Description
Contextual information plays a vital role for software developers when understanding and fixing a bug. Context can also be important in deep learning-based program repair to provide extra information about the bug and its fix. Existing techniques, however, treat context in an arbitrary manner, by extracting code in close proximity of the buggy statement within the enclosing file, class, or method, without any analysis to find actual relations with the bug. To reduce noise, they use a predefined maximum limit on the number of tokens to be used as context. We present a program slicing-based approach, in which instead of arbitrarily including code as context, we analyze statements that have a control or data dependency on the buggy statement. We propose a novel concept called dual slicing, which leverages the context of both buggy and fixed versions of the code to capture relevant repair ingredients. We present our technique and tool called KATANA, the first to apply slicing-based context for a program repair task. The results show KATANA effectively preserves sufficient information for a model to choose contextual information while reducing noise. We compare against four recent state-of-the-art context-aware program repair techniques. Our results show KATANA fixes between 1.5 to 3.7 times more bugs than existing techniques.
Genre
Thesis/Dissertation
Type
Text
Language
eng
Date Available
2022-12-06
Provider
Vancouver : University of British Columbia Library
Rights
Attribution-NonCommercial-NoDerivatives 4.0 International
DOI
10.14288/1.0422382
URI
http://hdl.handle.net/2429/83363
Degree
Master of Applied Science - MASc
Program
Electrical and Computer Engineering
Affiliation
Applied Science, Faculty of; Electrical and Computer Engineering, Department of
Degree Grantor
University of British Columbia
Graduation Date
2023-05
Campus
UBCV
Scholarly Level
Graduate
Rights URI
http://creativecommons.org/licenses/by-nc-nd/4.0/
Aggregated Source Repository
DSpace

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Attribution-NonCommercial-NoDerivatives 4.0 International