Published September 19, 2025
| Version 1.0
Dataset
Open
Supplemental material for: Large Language Models for Software Testing: A Research Roadmap
Creators
Contributors
Description
This repository contains the replication package of the paper Large Language Models for Software Testing: A Research Roadmap published at TO-DO
The replication package comprises the raw data used in the roadmap, as well as an interactive view hosted in GitHub Pages. In ZENODO, we made available the raw dataset curated by the different researchers in a CSV format, with a structure (columns) as follows:
- Id: Internal reference used in the study.
- Title: Title of the article.
- Year: Publication year or when it was made available on arXiv.
- Key: BibTeX key.
- Published into: Name of the journal or conference where it is published.
- Publication type: Type of publication: Conference, Journal, or arXiv.
- Bibtex: BibTeX entry of the publication.
- Type of work: Type of contribution: Survey, Comparison, and Research Contribution.
- Abstract: Short abstract retrieved from the article.
- Category: Type of LLM-based testing: Unit Test Generation, High-Level Test Generation, Oracle Generation, Test Augmentation or Improvement, Non-Functional Testing, and Test Agents.
- LLM-Approach: Type of LLM approach: LLM-Pure Prompting, Hybrid Prompting, LLM-Pure Fine-tune, and Hybrid Fine-tune.
- Benchmark: Name or identifier of the benchmark used.
- LLMs Used: Name of the model/models used in the article.
- Evaluation Metric: Name of the metrics used in evaluating the article.
- Tool: Name of the tool proposed by the article.
To cite this work:
Cristian Augusto, Antonia Bertolino, Guglielmo De Angelis, Francesca Lonetti, and Jesús Morán, “Large Language Models for Software Testing: A Research Roadmap”, Journal Name, XXX, YYY. https://doi.org/XXXXXX
Notes (Antigua and Barbuda Creole English)
Files
Papers.csv
Files
(312.0 kB)
| Name | Size | Download all |
|---|---|---|
|
md5:a2cbd672cd172d0f66586c41f7bd7a22
|
312.0 kB | Preview Download |
Additional details
Funding
- Ministerio de Ciencia, Innovación y Universidades
- MCIN/ AEI/10.13039/501100011033/FEDER PID2022-137646OB-C32
- European Commission
- MATISSE - Model-based engineering of Digital Twins for early verification and validation of Industrial Systems 101140216
- Ministero dell'università e della ricerca
- FAIR- FUTURE ARTIFICIAL INTELLIGENCE RESEARCH PE0000013
Dates
- Submitted
-
2025-09-26
Software
- Repository URL
- https://giis-uniovi.github.io/llm-testing-roadmap-rp/
- Programming language
- HTML
- Development Status
- Active