Optimizing test data generation using SI_CNNpro-enhanced MGA for mutation testing

doi:10.1016/j.jss.2025.112517

Optimizing test data generation using SI_CNNpro-enhanced MGA for mutation testing

2025-06-09

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DOI：10.1016/j.jss.2025.112517

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• Generates minimal path sets via graph-based bidirectional traversal, reducing test redundancy and resource consumption. • Integrates auxiliary features and incremental learning in CNN, improving prediction accuracy and model adaptability. • The mutation testing problem is transformed into an optimization problem. • Establish a multi-task optimization model of test data generation covering paths. • Proposes SI_CNNpro-enhanced MGA to optimize test data generation for mutation testing.

Keywords:

path sets

mutation testing

CNN

incremental learning

multi-task optimization

Optimizing test data generation using SI_CNNpro-enhanced MGA for mutation testing

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