Sustainable Training of Code Language Models through Data Refinement

Informacje na temat projektu

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Identyfikator umowy o grant: 101151798

DOI

10.3030/101151798

Projekt został zamknięty

Data podpisania przez KE 6 Marca 2024

Data rozpoczęcia 1 Kwietnia 2024

Data zakończenia 31 Marca 2026

Finansowanie w ramach

• Marie Skłodowska-Curie Actions (MSCA)

Koszt całkowity

Brak danych

Wkład UE

€ 210 911,04

Koordynowany przez

SIMULA RESEARCH LABORATORY AS
Norway

CORDIS oferuje możliwość skorzystania z odnośników do publicznie dostępnych publikacji i rezultatów projektów realizowanych w ramach programów ramowych HORYZONT.

Odnośniki do rezultatów i publikacji związanych z poszczególnymi projektami 7PR, a także odnośniki do niektórych konkretnych kategorii wyników, takich jak zbiory danych i oprogramowanie, są dynamicznie pobierane z systemu OpenAIRE .

Rezultaty

Documents, reports (1)

Communication, Dissemination & Exploitation Plan

The plan describes the planned measures to maximize the impact of the project, including the dissemination and exploitation measures that are planned, and the target group(s) addressed. Regarding communication measures and public engagement strategy, the aim is to inform and reach out to society and show the activities performed, and the use and the benefits the project will have for citizens.

Data Management Plan (1)

Data Management Plan

The Data Management Plan describes the data management life cycle for all data sets that will be collected, processed or generated by the action. It is a document describing what data will be collected, processed or generated and following what methodology and standards, whether and how this data will be shared and/or made open, and how it will be curated and preserved.

Publikacje

Conference proceedings (5)

Semantic-Preserving Transformations as Mutation Operators: A Study on Their Effectiveness in Defect Detection

Autorzy: Max Hort, Linas Vidziunas, Leon Moonen
Opublikowane w: 2025 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW), 2025
Wydawca: IEEE
DOI: 10.1109/ICSTW64639.2025.10962512

The Art of Repair: Optimizing Iterative Program Repair with Instruction-Tuned Models

Autorzy: Fernando Vallecillos Ruiz; Max Hort; Leon Moonen
Opublikowane w: Proceedings of the 29th International Conference on Evaluation and Assessment in Software Engineering, 2025
DOI: 10.48550/ARXIV.2505.02931

The Impact of Fine-Tuning Large Language Models on Automated Program Repair

Autorzy: Roman Macháček, Anastasiia Grishina, Max Hort, Leon Moonen
Opublikowane w: 2025 IEEE International Conference on Software Maintenance and Evolution (ICSME), 2025
Wydawca: IEEE
DOI: 10.1109/ICSME64153.2025.00042

Codehacks: A Dataset of Adversarial Tests for Competitive Programming Problems Obtained from Codeforces

Autorzy: Max Hort, Leon Moonen
Opublikowane w: 2025 IEEE Conference on Software Testing, Verification and Validation (ICST), 2025
Wydawca: IEEE
DOI: 10.1109/ICST62969.2025.10988963

A Comparative Study on Large Language Models for Log Parsing

Autorzy: Merve Astekin; Max Hort; Leon Moonen
Opublikowane w: Proceedings of the 18th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement, 2024
DOI: 10.48550/ARXIV.2409.02474

Peer reviewed articles (2)

Assessing the Latent Automated Program Repair Capabilities of Large Language Models using Round-Trip Translation

Autorzy: Fernando Vallecillos Ruiz, Anastasiia Grishina, Max Hort, Leon Moonen
Opublikowane w: ACM Transactions on Software Engineering and Methodology, 2025, ISSN 1049-331X
Wydawca: Association for Computing Machinery (ACM)
DOI: 10.1145/3771922

Fairst: A Novel Approach for Machine Learning Bias Repair Through Latent Sensitive Attribute Translation

Autorzy: Carmen Meinson; Max Hort; Federica Sarro
Opublikowane w: Information and Software Technology, 2025, ISSN 1873-6025
Wydawca: Elsevier B.V.
DOI: 10.1016/J.INFSOF.2025.107900