Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Seifert, Daniel | - |
dc.contributor.author | Jöckel, Lisa | - |
dc.contributor.author | Honroth, Thorsten | - |
dc.contributor.author | Trendowicz, Adam | - |
dc.contributor.author | Jedlitschk, Andreas | - |
dc.contributor.author | Ciolkowski, Marcus | - |
dc.date.accessioned | 2024-08-06T12:55:50Z | - |
dc.date.available | 2024-08-06T12:55:50Z | - |
dc.date.issued | 2024-08-01 | - |
dc.identifier.uri | https://fordatis.fraunhofer.de/handle/fordatis/411 | - |
dc.identifier.uri | http://dx.doi.org/10.24406/fordatis/359 | - |
dc.description.abstract | This is the data for the paper "Can Large Language Models (LLMs) Compete with Human Requirements Reviewers? – Replication of an Inspection Experiment on Requirements Documents". It contains the source code of the experiment to make our work transparent and reproducible. Furthermore, it contains the evaluation results. | en |
dc.language.iso | en | en |
dc.rights.uri | https://creativecommons.org/licenses/by-sa/4.0/ | en |
dc.subject | Large Language Models (LLMs) | en |
dc.subject | Requirements Engineering | en |
dc.subject | Inspection Experiment | en |
dc.subject | Python | en |
dc.subject | Quality Assessment | en |
dc.subject.ddc | DDC::000 Informatik, Informationswissenschaft, allgemeine Werke | en |
dc.title | Can Large Language Models (LLMs) compete with Human Requirement Reviewers? - Replication of an Inspection Experiment on Requirements Documents | en |
dc.type | Source Code | en |
dc.contributor.funder | Bundesministerium für Bildung und Forschung BMBF (Deutschland) | en |
dc.description.technicalinformation | The code is written in python. For each experiment there is one jupyter notebook. We used OpenAI models and open source models. The open source models were run on our infrastructure. To run the code using the open source models, it must be adapted to the infrastructure you have access to. | en |
fordatis.group | IUK-Technologie | en |
fordatis.institute | IESE Fraunhofer-Institut für Experimentelles Software Engineering | en |
fordatis.rawdata | false | en |
fordatis.sponsorship.projectid | 01IS23016D | en |
fordatis.sponsorship.projectname | DeepQuali - Anwendung von Deep Learning auf Software-Repositories zur Qualitätsbewertung | en |
fordatis.sponsorship.projectacronym | DeepQuali | en |
Appears in Collections: | Fraunhofer-Institut für Experimentelles Softwareengineering IESE |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
profes24_published_data.zip | Source code and evaluation results | 434,56 kB | ZIP | Download/Open |
This item is licensed under a Creative Commons License