'Columbia University Libraries/Information Services'
Doi
Abstract
The goal of this project is to demonstrate the feasibility of automatic detection of metamorphic properties of individual functions. Properties of interest here, as described in Murphy et al.'s SEKE 2008 paper "Properties of Machine Learning Applications for Use in Metamorphic Testing", include: 1. Permutation of the order of the input data 2. Addition of numerical values by a constant 3. Multiplication of numerical values by a constant 4. Reversal of the order of the input data 5. Removal of part of the data 6. Addition of data to the dataset While focusing on permutative, additive, and multiplicative properties in functions and applications, I have sought to identify common programming constructs and code fragments that strongly indicate that these properties will hold, or fail to hold, along an execution path in which the code is evaluated. I have constructed a syntax for expressions representing these common constructs and have also mapped a collection of these expressions to the metamorphic properties they uphold or invalidate. I have then developed a general framework to evaluate these properties for programs as a whole
