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PERSONA: A personalized model for code recommendation.

Tam The Nguyen1, Tung Thanh Nguyen1

  • 1Department of Computer Science and Software Engineering, Auburn University, Auburn, Alabama, United States of America.

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Summary
This summary is machine-generated.

This study introduces Persona, a personalized code recommendation model that learns individual programming patterns. Persona improves code suggestions by considering personal, project-specific, and common code patterns, outperforming existing methods.

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Area of Science:

  • Software Engineering
  • Artificial Intelligence
  • Human-Computer Interaction

Background:

  • Modern software development relies on code recommendation tools to boost programmer productivity.
  • Current crowd-based approaches aggregate code from many developers, potentially overlooking individual coding styles and preferences.
  • This aggregation can limit the accuracy of recommendations for specific users.

Purpose of the Study:

  • To develop a novel code recommendation technique that prioritizes individual programming patterns.
  • To introduce Persona, a personalized code recommendation model designed to capture unique developer habits.
  • To enhance code suggestion accuracy by integrating personal, project-specific, and common code patterns.

Main Methods:

  • Developed Persona, a model that learns personalized code patterns from a programmer's history.
  • Integrated personal patterns with project-specific and general code patterns for comprehensive recommendations.
  • Enabled recommendations for various code elements, including identifiers, methods, and parameters.

Main Results:

  • Empirical evaluations demonstrate the high effectiveness of the Persona-based recommendation tool.
  • The system achieved a top-1 accuracy of 60-65% in recommending the next identifier.
  • Persona significantly outperformed existing baseline code recommendation approaches.

Conclusions:

  • Personalized code recommendation is crucial for improving developer productivity.
  • Persona effectively captures and utilizes individual coding patterns for more accurate suggestions.
  • The proposed model offers a significant advancement over crowd-based methods in code recommendation.