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A User-friendly and Powerful R Analysis of Large-scale Datasets
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Software reusability dataset based on static analysis metrics and reuse rate information.

Michail D Papamichail1, Themistoklis Diamantopoulos1, Andreas L Symeonidis1

  • 1Intelligent Systems and Software Engineering Laboratory, Electrical and Computer Engineering Dept., Aristotle University of Thessaloniki, Greece.

Data in Brief
|November 14, 2019
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Summary

This study introduces a new dataset for evaluating software component reusability using data-driven methods. It analyzes reuse rates and static code metrics from open-source projects to inform future reusability assessments.

Keywords:
Code reuseDeveloper-perceived reusabilityReusability assessmentStatic analysis metrics

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Area of Science:

  • Software Engineering
  • Computer Science

Background:

  • Component-based development emphasizes software component reuse for success.
  • Traditional reusability evaluation relies on expert judgment.
  • The rise of open-source software enables data-driven approaches.

Purpose of the Study:

  • To generate a dataset for data-driven software component reusability evaluation.
  • To establish reuse rate as a measure of reusability.
  • To analyze static code properties in relation to reusability.

Main Methods:

  • Analyzed popular Maven registry projects.
  • Computed static analysis metrics (complexity, cohesion, coupling, inheritance, documentation, size) using SourceMeter.
  • Calculated component reuse rates using the AGORA code search engine.

Main Results:

  • Generated a dataset of over 24,000 classes and 2,000 packages.
  • The dataset links static analysis metrics with actual reuse rates.
  • Provides empirical data for reusability evaluation.

Conclusions:

  • The dataset supports the development of data-driven reusability evaluation methodologies.
  • Offers a foundation for understanding software component reusability beyond expert opinion.
  • Facilitates empirical research in software component reuse.