Replication package and dataset for a research paper on software architecture practices in ROS-based robotic systems.
ICSE-SEIP 2020 Replication Package is a research dataset and collection of scripts supporting a study on software architecture practices in ROS-based robotic systems. It includes mined GitHub repositories, survey responses, and analysis tools to understand how roboticists architect their systems. The package enables reproducibility and further research in robotics software engineering.
Researchers and academics in software engineering, robotics, or empirical studies who need to replicate, validate, or extend findings on architectural practices in ROS-based systems.
It provides a comprehensive, openly-licensed dataset with full traceability, including raw data, mining scripts, and analysis code, ensuring transparency and facilitating independent verification of the published research.
Replication package of the paper titled "How do you Architect your Robots? State of the Practice and Guidelines for ROS-based Systems" published at ICSE-SEIP 2020
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Includes all raw data, intermediate filtering results, and scripts, as shown in the dataset/ folder with CSV files and mining scripts, ensuring full traceability for verification.
Provides detailed replication steps in INSTALL.md for rebuilding the dataset and running analysis, aligning with the project's philosophy of transparency in research.
Offers PDFs like RQ1_codes_and_selection_criteria.pdf and online_questionnaire.pdf that explain selection criteria and survey processes, aiding in understanding the study design.
Licensed under MIT, allowing free use and modification for academic and commercial purposes, as stated in the License section.
Requires manual steps, multiple scripts (e.g., cloner.py, detector.py), and dependencies like R and Python, which can be time-consuming and error-prone for quick validation.
Based on 2020 data and specific to ROS-based systems, limiting relevance for newer ROS 2 projects or broader robotic frameworks without significant adaptation.
No evidence of active updates or community support; the static nature may lead to compatibility issues with modern tools or GitHub API changes.