Test Case Generation from Large Language Mode - Systematic Mapping Studies (2023 - 2025) - Code Results

This spreadsheet is the primary coding artifact of a Systematic Mapping Study analyzing 128 primary studies (2023–2025) on the use of Large Language Models (LLMs) in software testing. Each row corresponds to one study and records the prompt engineering technique(s) used (coded PE1–PE11), the fine-tuning strategy if any (coded F1–F7), the specific LLM(s) evaluated (coded M1–M11), and the testing lifecycle stage(s)/activity(ies) addressed. Stages and activities follow the taxonomy proposed in the associated paper. There are four lifecycle stages: T1 (Requirements Management), T2 (Artifact Generation), T3 (Verification and Prioritization), and T4 (Report and Regression). These are decomposed into thirteen activities (A1–A13, e.g., requirements extraction, element localization, test/input/oracle generation, runtime verification, prioritization, repair, refactoring, validation, quality assessment, log analysis). Cells in the "Stages/Activities" column list only the activities the LLM performs through reasoning, generation, or decision-making within each study's pipeline. This excludes steps done by deterministic auxiliary tools (parsers, static analyzers, symbolic execution engines, coverage tools) or by manual human evaluation, even when those steps are part of the same proposed technique.

Authors

Institutions

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-08-17
DOI
https://doi.org/10.5281/zenodo.21972875
Primary Topic
Software System Performance and Reliability
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Test Case Generation from Large Language Mode - Systematic Mapping Studies (2023 - 2025) - Code Results

Ricardo Vilela, Pedro Henrique Dias Valle, Leonardo Lino Gomes
Zenodo (CERN European Organization for Nuclear Research)
Software System Performance and Reliability
article

Test Case Generation from Large Language Mode - Systematic Mapping Studies (2023 - 2025) - Code Results

Ricardo Vilela, Pedro Henrique Dias Valle, Leonardo Lino Gomes
article en

Abstract

This spreadsheet is the primary coding artifact of a Systematic Mapping Study analyzing 128 primary studies (2023–2025) on the use of Large Language Models (LLMs) in software testing. Each row corresponds to one study and records the prompt engineering technique(s) used (coded PE1–PE11), the fine-tuning strategy if any (coded F1–F7), the specific LLM(s) evaluated (coded M1–M11), and the testing lifecycle stage(s)/activity(ies) addressed. Stages and activities follow the taxonomy proposed in the associated paper. There are four lifecycle stages: T1 (Requirements Management), T2 (Artifact Generation), T3 (Verification and Prioritization), and T4 (Report and Regression). These are decomposed into thirteen activities (A1–A13, e.g., requirements extraction, element localization, test/input/oracle generation, runtime verification, prioritization, repair, refactoring, validation, quality assessment, log analysis). Cells in the "Stages/Activities" column list only the activities the LLM performs through reasoning, generation, or decision-making within each study's pipeline. This excludes steps done by deterministic auxiliary tools (parsers, static analyzers, symbolic execution engines, coverage tools) or by manual human evaluation, even when those steps are part of the same proposed technique.

Zenodo (CERN European Organization for Nuclear Research)
Universidade de São Paulo (BR), Universidade Estadual de Campinas (UNICAMP) (BR)
Openalex Percentile: Top 7%
Software System Performance and Reliability
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.