Published September 19, 2025 | Version 1.0
Dataset Open

Supplemental material for: Large Language Models for Software Testing: A Research Roadmap

  • 1. ROR icon Universidad de Oviedo
  • 2. ROR icon Gran Sasso Science Institute
  • 3. ROR icon Istituto di Analisi dei Sistemi ed Informatica Antonio Ruberti
  • 4. ROR icon National Research Council
  • 1. ROR icon Universidad de Oviedo
  • 2. ROR icon Gran Sasso Science Institute
  • 3. ROR icon Istituto di Analisi dei Sistemi ed Informatica Antonio Ruberti
  • 4. ROR icon National Research Council

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)

The work was partially supported by the European HORIZON-KDT-JU research project MATISSE: "Model-based
engineering of Digital Twins for early verification and validation of Industrial Systems", HORIZON-KDT-JU-2023-2-RIA,
Proposal number: 101140216-2, KDT232RIA_00017, in part by the project PID2022-137646OB-C32 under Grant MCIN/
AEI/10.13039/501100011033/FEDER, UE, and also by the (partial) support of the PNRR MUR project FAIR (PE0000013).

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