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Genetic Programming Proof Search Automatic Improvement.

Zoltan A Kocsis1, Jerry Swan1

  • 1Department of Computer Science, The University of York, Deramore Lane, York, YO10 5GH UK.

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Summary
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This study introduces a novel semantics-preserving Genetic Improvement technique for software maintenance. It uses deterministic proof search to optimize code, enhancing program correctness and efficiency.

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Area of Science:

  • Computer Science
  • Software Engineering
  • Artificial Intelligence

Background:

  • Search-Based Software Engineering (SBSE) is crucial for software maintenance.
  • Genetic Improvement (GI) uses Genetic Programming for code optimization.
  • Existing GI methods often fail to preserve program semantics.

Purpose of the Study:

  • To develop a semantics-preserving alternative to traditional mutation operators in Genetic Improvement.
  • To apply deterministic proof search in sequent calculus for code transformations.
  • To enhance the 'grow and graft' technique in Genetic Improvement.

Main Methods:

  • Employed deterministic proof search within the sequent calculus framework.
  • Developed semantics-preserving transformations for algebraic data types.
  • Applied these methods to the 'grow and graft' technique in Genetic Improvement.

Main Results:

  • Successfully generated semantics-preserving transformations on algebraic data types.
  • Extended the expressiveness of the 'grafting' phase in Genetic Improvement.
  • Achieved asymptotic efficiency improvements by transforming list data type representations.

Conclusions:

  • The proposed deterministic proof search approach offers a robust alternative for semantics-preserving code optimization.
  • This method enhances the capabilities and correctness of Genetic Improvement techniques.
  • The case studies demonstrate practical applicability and performance benefits for software maintenance.