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ROS package search for robot software development: a knowledge graph-based approach

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Abstract

In recent years, ROS (Robot Operating System) packages have become increasingly popular as a type of software artifact that can be effectively reused in robotic software development. Indeed, finding suitable ROS packages that closely match the software’s functional requirements from the vast number of available packages is a nontrivial task using current search methods. The traditional search methods for ROS packages often involve inputting keywords related to robotic tasks into general-purpose search engines (e.g., Google) or code hosting platforms (e.g., GitHub) to obtain approximate results of all potentially suitable ROS packages. However, the accuracy of these search methods remains relatively low because the task-related keywords may not precisely match the functionalities offered by the ROS packages. To improve the search accuracy of ROS packages, this paper presents a novel semantic-based search approach that relies on the semantic-level ROS Package Knowledge Graph (RPKG) to automatically retrieve the most suitable ROS packages. Firstly, to construct the RPKG, we employ multi-dimensional feature extraction techniques to extract semantic concepts, including code file name, category, hardware device, characteristics, and function, from the dataset of ROS package text descriptions. The semantic features extracted from this process result in a substantial number of entities (32,294) and relationships (54,698). Subsequently, we create a robot domain-specific small corpus and further fine-tune a pre-trained language model, BERT-ROS, to generate embeddings that effectively represent the semantics of the extracted features. These embeddings play a crucial role in facilitating semantic-level understanding and comparisons during the ROS package search process within the RPKG. Secondly, we introduce a novel semantic matching-based search algorithm that incorporates the weighted similarities of multiple features from user search queries, which searches out more accurate ROS packages than the traditional keyword search method. To validate the enhanced accuracy of ROS package searching, we conduct comparative case studies between our semantic-based search approach and four baseline search approaches: ROS Index, GitHub, Google, and ChatGPT. The experiment results demonstrate that our approach achieves higher accuracy in terms of ROS package searching, outperforming the other approaches by at least 21% from 5 levels, including top1, top5, top10, top15, and top20.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant No. 62172426), the National Key R&D Program of China (2023YFB4503802) and the National University of Defense Technology Young Scientist Self-Innovation Science Fund Project (ZK2023-36).

Author information

Authors and Affiliations

  1. College of Computer, National University of Defense Technology, Changsha, 410000, China

    Shuo Wang, Xinjun Mao & Zhang Zhang

  2. Key Laboratory of Software Engineering for Complex Systems, Changsha, 410000, China

    Shuo Wang, Xinjun Mao & Zhang Zhang

  3. College of Systems Engineering, National University of Defense Technology, Changsha, 410000, China

    Shuo Yang

  4. College of Computer Science and Technology, Zhejiang University, Hangzhou, 310058, China

    Menghan Wu

Authors
  1. Shuo Wang
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Corresponding author

Correspondence to Xinjun Mao.

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Competing interests The authors declare that they have no competing interests or financial conflicts to disclose.

Additional information

Shuo Wang is a PhD graduate student in the College of Computer, National University of Defense Technology, China. His research interests include robotics software engineering, data mining, and knowledge graph.

Xinjun Mao is a professor in the College of Computer, National University of Defense Technology, China. He received his PhD degree in computer science from the National University of Defense Technology, China in 1998. His research interests include intelligent software engineering, multi-agent system, autonomous robot software, self-adaptive system, and crowdsourcing.

Shuo Yang is a lecturer in the College of Systems Engineering, National University of Defense Technology, China. He received his PhD degree in software engineering from National University of Defense Technology, China in 2022. His research interests include robotics software engineering, automated planning, and component-based robotic frameworks.

Menghan Wu is a PhD student at the College of Computer Science and Technology, Zhejiang University, China. Her current research interests include artificial intelligence for software engineering.

Zhang Zhang is a PhD graduate student in the College of Computer, National University of Defense Technology, China. His work interests include open source software engineering, data mining, and crowdsourced learning.

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Wang, S., Mao, X., Yang, S. et al. ROS package search for robot software development: a knowledge graph-based approach. Front. Comput. Sci. 19, 196320 (2025). https://doi.org/10.1007/s11704-024-3660-9

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  • DOI: https://doi.org/10.1007/s11704-024-3660-9

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