Sustainable Training of Code Language Models through Data Refinement

Projektinformationen

condenSE

ID Finanzhilfevereinbarung: 101151798

DOI

10.3030/101151798

Projekt abgeschlossen

EK-Unterschriftsdatum 6 März 2024

Startdatum 1 April 2024

Enddatum 31 März 2026

Finanziert unter

• Marie Skłodowska-Curie Actions (MSCA)

Gesamtkosten

Keine Daten

EU-Beitrag

€ 210 911,04

Koordiniert durch

SIMULA RESEARCH LABORATORY AS
Norway

CORDIS bietet Links zu öffentlichen Ergebnissen und Veröffentlichungen von HORIZONT-Projekten.

Links zu Ergebnissen und Veröffentlichungen von RP7-Projekten sowie Links zu einigen Typen spezifischer Ergebnisse wie Datensätzen und Software werden dynamisch von OpenAIRE abgerufen.

Leistungen

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.

Veröffentlichungen

Conference proceedings (5)

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

Autoren: Max Hort, Linas Vidziunas, Leon Moonen
Veröffentlicht in: 2025 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW), 2025
Herausgeber: IEEE
DOI: 10.1109/ICSTW64639.2025.10962512

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

Autoren: Fernando Vallecillos Ruiz; Max Hort; Leon Moonen
Veröffentlicht in: 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

Autoren: Roman Macháček, Anastasiia Grishina, Max Hort, Leon Moonen
Veröffentlicht in: 2025 IEEE International Conference on Software Maintenance and Evolution (ICSME), 2025
Herausgeber: IEEE
DOI: 10.1109/ICSME64153.2025.00042

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

Autoren: Max Hort, Leon Moonen
Veröffentlicht in: 2025 IEEE Conference on Software Testing, Verification and Validation (ICST), 2025
Herausgeber: IEEE
DOI: 10.1109/ICST62969.2025.10988963

A Comparative Study on Large Language Models for Log Parsing

Autoren: Merve Astekin; Max Hort; Leon Moonen
Veröffentlicht in: 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

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

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

Autoren: Carmen Meinson; Max Hort; Federica Sarro
Veröffentlicht in: Information and Software Technology, 2025, ISSN 1873-6025
Herausgeber: Elsevier B.V.
DOI: 10.1016/J.INFSOF.2025.107900