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Graph semantic similarity-based automatic assessment for programming exercises.

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Summary
This summary is machine-generated.

This study introduces an efficient algorithm for automatic programming exercise assessment. It evaluates code structure and semantics without running programs, offering reliable and accurate scoring for multiple languages.

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Area of Science:

  • Computer Science
  • Software Engineering
  • Educational Technology

Background:

  • Traditional programming exercise assessment often requires program execution, which can be computationally intensive and prone to environmental issues.
  • Existing automated grading systems may struggle with semantic understanding and structural variations in student code.
  • There is a need for efficient and accurate automated assessment methods that can handle diverse programming languages.

Purpose of the Study:

  • To propose a novel algorithm for automatic assessment of programming exercises.
  • To develop a method that scores programs based on structural and semantic similarity without execution.
  • To enhance the reliability and accuracy of automated programming assessment.

Main Methods:

  • The algorithm analyzes program dependency graph structures and semantic similarities.
  • It calculates node similarity between student and reference programs.
  • A similarity matrix is generated to identify the optimal similarity node path.

Main Results:

  • The proposed algorithm achieves improved computational efficiency with a time complexity of O(n^2).
  • Experimental results demonstrate higher reliability and accuracy compared to existing algorithms.
  • The method is effective for assessing programming exercises in C/C++, Java, and Python.

Conclusions:

  • The developed algorithm provides an efficient and effective approach to automated programming exercise assessment.
  • It offers a reliable alternative to execution-based grading systems.
  • The algorithm's language-agnostic design makes it broadly applicable in computer science education.