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An Intelligent Platform for Software Component Mining and Retrieval.

Nazia Bibi1, Tauseef Rana1, Ayesha Maqbool1

  • 1Department of Computer Software Engineering, National University of Sciences and Technology, Islamabad 44000, Pakistan.

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|January 8, 2023
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Summary

This study introduces a novel platform for robotic application development, using machine learning to recommend reusable code components. The system efficiently ranks source code snippets, simplifying development and outperforming existing methods.

Keywords:
code recommendationcode reusecomponent-based software developmentintelligent decision support systemrecommendation systems

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Area of Science:

  • Robotics
  • Software Engineering
  • Computer Science

Background:

  • Developing robotic, IoT, and sensor-based applications is complex and time-consuming.
  • Effective utilization of software components is crucial for these applications.
  • Existing development frameworks lack efficient code reuse mechanisms.

Purpose of the Study:

  • To propose a platform that efficiently searches and recommends reusable code components for robotic applications.
  • To streamline the development process by facilitating code component reuse.
  • To enhance the accessibility and adaptability of programming frameworks for robotics.

Main Methods:

  • Developed a platform employing a machine learning approach to train a schema for locating and ranking source code snippets.
  • Implemented a user-friendly interface for developers to input queries (specifications) for code search.
  • Utilized the trained schema to rank code snippets within the top k results.

Main Results:

  • The platform effectively ranks source code snippets based on relevance to developer queries.
  • The proposed approach demonstrates superior performance compared to existing baseline methods.
  • Evaluation results confirm the platform's efficiency in recommending suitable code components.

Conclusions:

  • The developed platform significantly facilitates the reuse of code components in robotic application development.
  • The machine learning-based approach for schema training and ranking enhances development efficiency.
  • A user survey validated the practical viability and effectiveness of the proposed methodology.