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# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!
cff-version: 1.2.0
title: >-
Divide and Conquer the EmpiRE: A Community-Maintainable
Knowledge Graph of Empirical Research in Requirements
Engineering - A Sustainable Literature Review for
Analyzing the State and Evolution of Empirical Research in
Requirements Engineering
message: >-
If you use this software, please cite it using the
metadata from this file.
type: software
authors:
- given-names: Oliver
family-names: Karras
email: [email protected]
affiliation: >-
TIB - Leibniz Information Centre for Science and
Technology
orcid: 'https://orcid.org/0000-0001-5336-6899'
identifiers:
- type: doi
value: 10.1109/ESEM56168.2023.10304795
description: >-
The DOI of the ESEM 2023 publication on IEEE
Xplore.
- type: doi
value: 10.48550/arXiv.2306.16791
description: The DOI of the ESEM 2023 preprint on arXiv.
- type: doi
value: 10.5281/zenodo.8083528
description: The DOI of the corresponding release on Zenodo.
- type: doi
value: 10.1109/RE59067.2024.00063
description: The DOI of the RE 2024 publication on IEEE Xplore.
- type: doi
value: 10.48550/arXiv.2405.08351
description: The DOI of the RE 2024 preprint on arXiv.
repository-code: 'https://github.com/okarras/EmpiRE-Analysis'
url: >-
https://orkg.org/observatory/Empirical_Software_Engineering?sort=combined&classesFilter=Paper,Comparison,Visualization
abstract: >
This project contains the constantly updated data,
analysis, and results of a sustainanle literature review
on the state and evolution of empirical research in
requirements engineering (RE) using the developed
KG-EmpiRE.
KG-EmpiRE is a community-maintainable knowledge graph (KG)
of empirical research in requirements engineering based on
scientific data extracted from currently 680 papers
published in the research track of the IEEE International
Conference on Requirement Engineering from 1994 to 2022.
We are currently organizing scientific data in KG-EmpiRE
using a defined template for the six themes of research
paradigm, research design, research method, data
collection, data analysis and bibliographic metadata with
the long-term plan to expand the themes.
KG-EmpiRE itself is maintained in the Open Research
Knowledge Graph (ORKG). The ORKG is a cross-domain and
cross-topic research knowledge graph (RKG) with a
corresponding technical infrastructure and services for
the organization of Findable, Accessible, Interoperable,
and Reusable (FAIR) scientific data from papers in
accordance with the FAIR data principles. The TIB -
Leibniz Information Centre for Science and Technology
developes and maintains the ORKG permaently and has
committed itself to the long-term archiving of all data.
As a central accees point to all curated papers in
KG-EmpiRE, we established a more general ORKG observatory
on empirical research in software engineering. In
addition, we add a RDF dump of the ORKG to each release of
the project published on Zenodo, which includes the most
recent analyzed data from KG-EmpiRE.
In this project, we perform the data analysis of
KG-EmpiRE, which has two purposes:
1. We evaluate the coverage of the curated topic of empirical research in RE by KG-EmpiRE.
2. We gain insights into the state and evolution of empirical research in RE.
The data analysis is based on competency questions
regarding empirical research in SE, including RE, derived
from the vision of Sjøberg et al. (2007). Sjøberg et al.
(2007) describe their vision of the role of empirical
methods in SE, including RE, for the period of 2020 – 2025
by comparing the "current" state of practice (2007) with
their target state (2020 - 2025). We analyzed these
descriptions and derived a total of 77 competency
questions. The number of competency questions answered
reflects the coverage of the curated topic in KG-EmpiRE
(1), and the answers to competency questions provide
insights into the state and evolution of empirical
research in RE (2). For each competency question that can
be answered with KG-EmpiRE (currently 16 of 77), we
specified a SPARQL query to retrieve and analyze the data
of KG-EmpiRE from the ORKG. We provide all details of the
analysis with its SPARQL queries, data, visualizations,
and explanations in the Jupyter Notebook hosted on
mybinder for interactive reproduction and (re-)use, always
using the most recent data from KG-EmpiRE.
The analysis of the individual competency questions always
follows the same structure:
1. Data Selection: Explaining the competency question and the required data for the analysis.
2. Data Collection: Executing the specified SPARQL query to retrieve the data.
3. Data Exploration: Exploring the data, including its cleaning and validation, to prepare the data for data analysis.
4. Data Analysis: Analyzing the data and creating visualizations.
5. Data Interpretation: Interpreting the data and derive insights.
Overall, this project serves to make the data, analysis,
and results openly available in the long term according to
the FAIR data principles to enable a reproducible,
(re-)usable and thus sustainable literature review.
In this way, this project can be used to:
1. Reproduce the results from the Related Publication.
2. (Re-)use KG-EmpiRE with its data for other research on empirical research in RE.
3. Replicate our research approach for sustainable literature reviews on other topics.
keywords:
- Python
- Jupyter notebook
- Analysis
- Empirical research
- Requirements engineering
- Open Research Knowledge Graph
license: MIT
commit: 77e4cd8
version: v1.1
date-released: '2024-04-30'