Counterexample guided program repair using zero-shot learning and MaxSAT-based fault localization

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F25%3A00387612" target="_blank" >RIV/68407700:21730/25:00387612 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1609/aaai.v39i1.32046" target="_blank" >https://doi.org/10.1609/aaai.v39i1.32046</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1609/aaai.v39i1.32046" target="_blank" >10.1609/aaai.v39i1.32046</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Counterexample guided program repair using zero-shot learning and MaxSAT-based fault localization

Popis výsledku v původním jazyce

Automated Program Repair (APR) for introductory programming assignments (IPAS) is motivated by the large number of student enrollments in programming courses each year. Since providing feedback on programming assignments requires substantial time and effort from faculty, personalized automated feedback often involves suggesting repairs to students' programs. Symbolic semantic repair approaches, which rely on Formal Methods (FM), check a program's execution against a test suite or reference solution, are effective but limited. These tools excel at identifying buggy parts but can only fix programs if the correct implementation and the faulty one share the same control flow graph. Conversely, Large Language Models (LLMS) are used for program repair but often make extensive rewrites instead of minimal adjustments. This tends to lead to more invasive fixes, making it harder for students to learn from their mistakes. In summary, LLMS excel at completing strings, while FM-based fault localization excel at identifying buggy parts of a program.

Název v anglickém jazyce

Counterexample guided program repair using zero-shot learning and MaxSAT-based fault localization

Popis výsledku anglicky

Automated Program Repair (APR) for introductory programming assignments (IPAS) is motivated by the large number of student enrollments in programming courses each year. Since providing feedback on programming assignments requires substantial time and effort from faculty, personalized automated feedback often involves suggesting repairs to students' programs. Symbolic semantic repair approaches, which rely on Formal Methods (FM), check a program's execution against a test suite or reference solution, are effective but limited. These tools excel at identifying buggy parts but can only fix programs if the correct implementation and the faulty one share the same control flow graph. Conversely, Large Language Models (LLMS) are used for program repair but often make extensive rewrites instead of minimal adjustments. This tends to lead to more invasive fixes, making it harder for students to learn from their mistakes. In summary, LLMS excel at completing strings, while FM-based fault localization excel at identifying buggy parts of a program.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the 39th AAAI Conference on Artificial Intelligence

ISBN

978-1-57735-897-8

ISSN

2159-5399

e-ISSN

2374-3468

Počet stran výsledku

9

Strana od-do

649-657

Název nakladatele

AAAI Press

Místo vydání

Menlo Park

Místo konání akce

Philadelphia

Datum konání akce

27. 2. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001478149100073