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Selecting optimal software code descriptors-The case of Java.

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Selecting the right software metrics is crucial for project success. This study identifies optimal subsets of software engineering metrics using advanced algorithms, significantly reducing complexity and improving project management for Java programs.

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Area of Science:

  • Software Engineering
  • Computer Science
  • Data Science

Background:

  • Proliferation of software metrics and tools presents challenges in selection and management.
  • Existing metrics can suffer from theoretical issues like collinearity and overfitting.
  • Practical management of numerous metrics is difficult, especially for smaller companies.

Purpose of the Study:

  • To identify a viable subset of software metrics for effective software project and product management.
  • To address the theoretical and practical challenges posed by the abundance of software metrics.
  • To develop and validate a method for selecting optimal metric subsets for Java programs.

Main Methods:

  • Utilized Particle Swarm Optimization and Genetic Algorithm to identify optimal metric subsets.
  • Focused on Java programs, analyzing metrics at class and method levels.
  • Employed Sammon error as a measure of metric similarity.

Main Results:

  • Experimented on 800 GitHub projects and validated on 200 projects.
  • Achieved optimal subsets of software engineering metrics.
  • Demonstrated low Sammon error values (>70%) at class and method levels on the validation dataset.

Conclusions:

  • The proposed method effectively identifies optimal subsets of software metrics.
  • These subsets significantly reduce complexity and improve manageability for software projects.
  • The approach provides a viable solution for selecting metrics crucial for project and product management.