Graph Neural Networks for Mapping Variables Between Programs
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F23%3A00372129" target="_blank" >RIV/68407700:21730/23:00372129 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.3233/FAIA230468" target="_blank" >https://doi.org/10.3233/FAIA230468</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.3233/FAIA230468" target="_blank" >10.3233/FAIA230468</a>
Alternative languages
Result language
angličtina
Original language name
Graph Neural Networks for Mapping Variables Between Programs
Original language description
Automated program analysis is a pivotal research domain in many areas of Computer Science — Formal Methods and Artificial Intelligence, in particular. Due to the undecidability of the problem of program equivalence, comparing two programs is highly challenging. Typically, in order to compare two programs, a relation between both programs’ sets of variables is required. Thus, mapping variables between two programs is useful for a panoply of tasks such as program equivalence, program analysis, program repair, and clone detection. In this work, we propose using graph neural networks (GNNs) to map the set of variables between two programs based on both programs’ abstract syntax trees (ASTs). To demonstrate the strength of variable mappings, we present three use-cases of these mappings on the task of program repair to fix well-studied and recurrent bugs among novice programmers in introductory programming assignments (IPAs). Experimental results on a dataset of 4166 pairs of incorrect/correct programs show that our approach correctly maps 83% of the evaluation dataset. Moreover, our experiments show that the current state-of-the-art on program repair, greatly dependent on the programs’ structure, can only repair about 72% of the incorrect programs. In contrast, our approach, which is solely based on variable mappings, can repair around 88.5%.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
European Conference on Artificial Intelligence 2023
ISBN
978-1-64368-436-9
ISSN
0922-6389
e-ISSN
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Number of pages
8
Pages from-to
1811-1818
Publisher name
IOS Press
Place of publication
Amsterdam
Event location
Krakov
Event date
Sep 30, 2023
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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