Metamorphic relationship responsiveness based on improved K-means algorithm

Abstract

Testing machine learning programs is challenging and costly using existing methods. Among them, metamorphic testing reduces the cost by verifying that metamorphic relationships are satisfied between test cases and outputs. The core of metamorphic testing is the metamorphic relationship, and the currently used Failure Detecting (FDR) fails to fully reveal its key properties in terms of error detection. Therefore, this paper proposes the concept of metamorphic relationship responsiveness, which comprehensively responds to the characteristics of metamorphic relationships by expressing the error detection rate of each metamorphic relationship as a multi-dimensional vector, thus providing more possibilities for machine learning research. And the optimized K-means algorithm is used for clustering analysis of metamorphic relationships, which mines out the association between metamorphic relationship construction requirements and error discovery ability. Experiments show that the metamorphic relationship reactivity can well support the cluster analysis and mine effective knowledge.