This project contains the constantly updated data, analysis, and results of a sustainable 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 776 papers published in the research track of the IEEE International Conference on Requirement Engineering from 1993 to 2025. 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 develops and maintains the ORKG permanently and has committed itself to the long-term archiving of all data. As a central access point to all curated papers in KG-EmpiRE, we established a more general ORKG observatory on empirical research in software engineering. In addition, the ORKG provides a RDF dump of all its data, including the most recent data from KG-EmpiRE. We also store the data used for analysis as CSV files. Note: For each related publication, we provide a folder containing the respective CSV files to enable the replication of the results. The details on the replication of the results can be found in the usage instructions. 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). They 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 SPARQL queries 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 binder for interactive replication and (re-)use, always using the most recent data from KG-EmpiRE. In addition, we developed the neuro-symbolic dashboard EmpiRE-Compass that is designed to support the sustainable synthesis, discovery, and reuse of scientific knowledge about empirical research on Requirements Engineering (RE) using ORKG and large language models. The analysis of the individual competency questions always follows the same structure: Data Selection: Explaining the competency question and the required data for the analysis. Data Collection: Executing the specified SPARQL query to retrieve the data. Data Exploration: Exploring the data, including its cleaning and validation, to prepare the data for data analysis. Data Analysis: Analyzing the data and creating visualizations. 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 replicable, (re-)usable, and thus sustainable literature review. In this way, this project can be used for: Replication of the results from the related publications. (Re-)use of KG-EmpiRE with its most recent data. Repetition of our research approach for sustainable literature reviews on other topics.

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