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ZENODO
Dataset . 2024
License: CC BY
Data sources: ZENODO
ZENODO
Dataset . 2024
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2024
License: CC BY
Data sources: Datacite
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Supplemental material for: Software System Testing assisted by Large Language Models: An Exploratory Study

Authors: Augusto, Cristian; Morán Barbón, Jesús; Bertolino, Antonia; Riva Alvarez, Claudio de la; Tuya, Javier;

Supplemental material for: Software System Testing assisted by Large Language Models: An Exploratory Study

Abstract

This is the supplemental material of the paper titled as “Software System Testing Assisted by Large Language Models: An Exploratory Study” presented at the 36th International Conference on Testing Software and Systems. It contains the raw execution data generated by both models, GPT-4o and GPT-4omini, during the exploratory study. The supplementary material includes the following files: GPT-4ominiRQ1-2ExecutionData.zip: contains the JSON outputs from the OpenAI API for the GPT-4o mini model. Each output is labeled according to the research question number and the corresponding timestamp (for RQ1) or the requested test case (for RQ2), all provided in plain text format. GPT-4oRQ1-2ExecutionData.zip: contains the JSON outputs from the OpenAI API for the GPT-4o model. Like the previous file, each output is named in plain text format based on the research question number and timestamp (for RQ1) or the requested test case (for RQ2). To cite this work: C. Augusto, J. Morán, A. Bertolino, C. de la Riva and J. Tuya, “Software System Testing assisted by Large Language Models: An Exploratory Study”, in 36th International Conference on Testing Software and Systems, London (England), XXXX, November 2024, doi: XXXXX

This work was supported in part by the project PID2022-137646OB-C32 under Grant MCIN/ AEI/10.13039/501100011033/FEDER, UE, and in part by the project MASE RDS-PTR_22_24_P2.1 Cybersecurity (Italy).

Keywords

Testing, Large Language Models (LLMs), Software Engineering

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average