Sustainable Training of Code Language Models through Data Refinement

Informazioni relative al progetto

condenSE

ID dell’accordo di sovvenzione: 101151798

DOI

10.3030/101151798

Progetto chiuso

Data della firma CE 6 Marzo 2024

Data di avvio 1 Aprile 2024

Data di completamento 31 Marzo 2026

Finanziato da

• Marie Skłodowska-Curie Actions (MSCA)

Costo totale

Nessun dato

Contributo UE

€ 210 911,04

Coordinato da

SIMULA RESEARCH LABORATORY AS
Norway

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I link ai risultati e alle pubblicazioni dei progetti del 7° PQ, così come i link ad alcuni tipi di risultati specifici come dataset e software, sono recuperati dinamicamente da .OpenAIRE .

Risultati finali

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.

Pubblicazioni

Conference proceedings (5)

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

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

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

Autori: Fernando Vallecillos Ruiz; Max Hort; Leon Moonen
Pubblicato 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

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

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

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

A Comparative Study on Large Language Models for Log Parsing

Autori: Merve Astekin; Max Hort; Leon Moonen
Pubblicato 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

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

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

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