All

What are you looking for?

All
Projects
Results
Organizations

Quick search

  • Projects supported by TA ČR
  • Excellent projects
  • Projects with the highest public support
  • Current projects

Smart search

  • That is how I find a specific +word
  • That is how I leave the -word out of the results
  • “That is how I can find the whole phrase”

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

The result's identifiers

  • Result code in 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>

  • Result on the web

    <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>

Alternative languages

  • Result language

    angličtina

  • Original language name

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

  • Original language description

    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.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • 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

    2025

  • Confidentiality

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

Data specific for result type

  • Article name in the collection

    Proceedings of the 39th AAAI Conference on Artificial Intelligence

  • ISBN

    978-1-57735-897-8

  • ISSN

    2159-5399

  • e-ISSN

    2374-3468

  • Number of pages

    9

  • Pages from-to

    649-657

  • Publisher name

    AAAI Press

  • Place of publication

    Menlo Park

  • Event location

    Philadelphia

  • Event date

    Feb 27, 2025

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article

    001478149100073